The medical complexity of surgical patients is increasing, and surgical risk calculators are crucial in providing high-value, patient-centered surgical care. However, pre-existing models are not validated to accurately predict risk for major gynecological oncology surgeries, and many are not generalizable to low- and middle-income country settings (LMICs). The international GO SOAR database dataset was used to develop a novel predictive surgical risk calculator for post-operative morbidity and mortality following gynecological surgery. Fifteen candidate features readily available pre-operatively across both high-income countries (HICs) and LMICs were selected. Predictive modeling analyses using machine learning methods and linear regression were performed. The area-under-the-receiver-operating characteristic curve (AUROC) was calculated to assess overall discriminatory performance. Neural networks (AUROC 0.94) significantly outperformed other models (p < 0.001) for evaluating the accuracy of prediction across three groups, i.e., minor morbidity (Clavien–Dindo I-II), major morbidity (Clavien–Dindo III-V), and no morbidity. Logistic-regression modeling outperformed the clinically established SORT model in predicting mortality (AUROC 0.66 versus 0.61, p < 0.001). The GO SOAR surgical risk prediction model is the first that is validated for use in patients undergoing gynecological surgery. Accurate surgical risk predictions are vital within the context of major cytoreduction surgery, where surgery and its associated complications can diminish quality-of-life and affect long-term cancer survival. A model that requires readily available pre-operative data, irrespective of resource setting, is crucial to reducing global surgical disparities. Full article

Investigating pharmacovigilance (PV) practices among oncology healthcare providers (HCPs) is crucial for patient safety in oncology settings. This study aimed to assess the awareness, attitudes, and practices towards PV and identify barriers to effective adverse drug reaction (ADR) reporting for HCPs working in oncology-related settings. Employing a cross-sectional survey design, we collected data from 65 HCPs, focusing on their experiences with ADR reporting, education on ADR management, and familiarity with PV protocols. The results showed that about half of the responders were pharmacists. Around 58.9% of the respondents reported ADRs internally, and 76.9% had received some form of ADR-related education. However, only 38.5% were aware of formal ADR review procedures. Methotrexate and paclitaxel emerged as the drugs most frequently associated with ADRs. The complexity of cancer treatments was among the common reasons for the low reporting of ADRs by the study participants. The findings highlight the need for enhanced PV education and standardized reporting mechanisms to improve oncology care. We conclude that reinforcing PV training and streamlining ADR-reporting processes are critical to optimizing patient outcomes and safety in oncology, advocating for targeted educational interventions and the development of unified PV guidelines. Full article

PURPOSE. Different combination modalities between an anti-PD-1/PD-L1 agent and a platinum-based chemotherapy or another checkpoint inhibitor (with or without a short course or full course of a platinum doublet) proved superior to chemotherapy alone in multiple clinical trials, but these strategies were not directly compared. The aim of this study is to report the real-world data results with different immunotherapy combinations in a series of patients treated in consecutive cohorts at the Ion Chiricuță Oncology Institute. METHODS. A total of 122 patients were successively enrolled in three cohorts: (1A) nivolumab + ipilimumab (18 patients), (1B) nivolumab + ipilimumab + short-course chemotherapy (33 patients), and (2) pembrolizumab plus full-course chemotherapy (71 patients). Endpoints included overall survival (OS), progression-free survival (PFS), objective response (ORR), and univariate and multivariate exploratory analysis of prognostic factors. RESULTS. Median follow-up in the consecutive cohorts 1A, 1B, and 2 was 83 versus 59 versus 14.2 months. Median OS and PFS for all patients were 22.2 and 11.5 months, respectively, and 2-year actuarial OS and PFS were 49% and 35%, respectively. For the nivolumab + ipilimumab (cohorts 1A and 1B) versus pembrolizumab combinations (cohort 2), median OS was 14 vs. 24.8 months (p = 0.18) and 2-year actuarial survival 42% vs. 53%; median PFS was 8.6 vs. 12.7 months (p = 0.41) and 2-year actuarial PFS 34% vs. 35%; response rates were 33.3% vs. 47.9% (p = 0.22). Older age, impaired PS (2 versus 0–1), corticotherapy in the first month of immunotherapy, and >3.81 neutrophils to lymphocytes ratio were independent unfavorable prognostic factors in the multivariate analysis of survival (limited to 2 years follow-up). The 5-year long-term survival was 30.5% and 18.8% for cohorts 1A and 1B, respectively (not enough follow-up for cohort 2). CONCLUSIONS. Efficacy results using different immunotherapy combination strategies were promising and not significantly different between protocols at 2 years. Real-world efficacy and long-term results in our series were in line with those reported in the corresponding registration trials. Full article

Malignant pleural mesothelioma (MPM) represents a significant health burden, with limited treatment options and poor prognosis. Despite advances in pharmacological and surgical interventions, the role of rehabilitation in MPM management remains underexplored. This study aims to assess the feasibility of a tailored pulmonary rehabilitation intervention addressing physical and respiratory function in MPM patients. A prospective pilot study was conducted on surgically treated MPM patients referred to a cardiopulmonary rehabilitation service. The intervention comprised multidisciplinary educational sessions, physical rehabilitation, and respiratory physiotherapy. Feasibility was evaluated based on dropout rates, adherence to the rehabilitation program, safety, and patient-reported outcomes. Twelve patients were initially enrolled, with seven completing the study. High adherence to physical (T1: 93.43%, T2: 82.56%) and respiratory (T1: 96.2%, T2: 92.5%) rehabilitation was observed, with minimal adverse events reported. Patient satisfaction remained high throughout the study (GPE scores at T1: 1.83 ± 1.17; T2: 2.0 ± 1.15), with improvements noted in physical function, pain management, and health-related quality of life. However, some issues, such as time constraints and lack of continuous supervision, were reported by participants. This pilot study demonstrates the feasibility and potential benefits of a tailored pulmonary rehabilitation intervention in MPM patients. Despite its promising outcomes, further research with larger samples is warranted to validate its efficacy and integrate rehabilitation as a component into the multidisciplinary management of MPM. Full article

Monitoring the genetic variability of human respiratory syncytial virus (hRSV) is of paramount importance, especially for the potential implication of key antigenic mutations on the emergence of immune escape variants. Thus, to describe the genetic diversity and evolutionary dynamics of hRSV circulating in Sicily (Italy), a total of 153 hRSV whole-genome sequences collected from 770 hRSV-positive subjects between 2017 and 2023, before the introduction of expanded immunization programs into the population, were investigated. The phylogenetic analyses indicated that the genotypes GA.2.3.5 (ON1) for hRSV-A and GB.5.0.5a (BA9) for hRSV-B co-circulated in our region. Amino acid (AA) substitutions in the surface and internal proteins were evaluated, including the F protein antigenic sites, as the major targets of immunoprophylactic monoclonal antibodies and vaccines. Overall, the proportion of AA changes ranged between 1.5% and 22.6% among hRSV-A, whereas hRSV-B varied in the range 0.8–16.9%; the latter was more polymorphic than hRSV-A within the key antigenic sites. No AA substitutions were found at site III of both subgroups. Although several non-synonymous mutations were found, none of the polymorphisms known to potentially affect the efficacy of current preventive measures were documented. These findings provide new insights into the global hRSV molecular epidemiology and highlight the importance of defining a baseline genomic picture to monitor for future changes that might be induced by the selective pressures of immunological preventive measures, which will soon become widely available. Full article

The adoption of environmental conservation agriculture (ECA) in Nepal is aligned with the country’s goal to achieve carbon neutrality by 2045, as ECA practices have been proven to effectively reduce greenhouse gas emissions. Nepal’s agricultural sector faces numerous challenges, including labor shortages, climate change impacts, and the necessity for environmentally friendly farming methods, making the adoption of ECA practices even more crucial. Thus, this paper explored farmer perspectives on the sustainability of ECA practices in the Namobuddha municipality, Nepal, which is renowned as a leading hub of organic farming. A cross-sectional survey was conducted, together with key informant interviews and onsite observations. By analyzing various farmer perspectives, the study presents an analytical framework that highlights the economic, environmental, and social pillars of ECA’s sustainability. The findings underscore the significance of economic viability for farmers, as damages to crops and farm products negatively drive their perception of ECA sustainability. Conversely, factors such as increased agriculture-related income, favorable prices, and sustainable productivity positively shape farmers’ perceptions. In terms of environmental sustainability, farmers prioritize enhancing the local and global environment, viewing their farming methods as climate-smart and actively working towards reducing greenhouse gas emissions. The study emphasizes the importance of strategic communication to effectively convey the benefits of ECA to rural communities. Overall, this research contributes to filling the knowledge gap concerning farmers’ perceptions of ECA sustainability. The insights gained from this study have the potential to inform policy decisions and promote the widespread adoption of environmentally friendly farming practices in Nepal. Full article

Background and Objectives: Currently, no tool exists to predict clinical outcomes in patients with advanced Parkinson’s disease (PD) under levodopa–carbidopa intestinal gel (LCIG) treatment. The aim of this study was to develop a novel deep neural network model to predict the clinical outcomes of patients with advanced PD after two years of LCIG therapy. Materials and Methods: This was a longitudinal, 24-month observational study of 59 patients with advanced PD in a multicenter registry under LCIG treatment from September 2019 to September 2021, including 43 movement disorder centers. The data set includes 649 measurements of patients, which make an irregular time series, and they are turned into regular time series during the preprocessing phase. Motor status was assessed with the Unified Parkinson’s Disease Rating Scale (UPDRS) Parts III (off) and IV. The NMS was assessed by the NMS Questionnaire (NMSQ) and the Geriatric Depression Scale (GDS), the quality of life by PDQ-39, and severity by Hoehn and Yahr (HY). Multivariate linear regression, ARIMA, SARIMA, and Long Short-Term Memory–Recurrent NeuralNetwork (LSTM-RNN) models were used. Results: LCIG significantly improved dyskinesia duration and quality of life, with men experiencing a 19% and women a 10% greater improvement, respectively. Multivariate linear regression models showed that UPDRS-III decreased by 1.5 and 4.39 units per one-unit increase in the PDQ-39 and UPDRS-IV indexes, respectively. Although the ARIMA-(2,0,2) model is the best one with AIC criterion 101.8 and validation criteria MAE = 0.25, RMSE = 0.59, and RS = 0.49, it failed to predict PD patients’ features over a long period of time. Among all the time series models, the LSTM-RNN model predicts these clinical characteristics with the highest accuracy (MAE = 0.057, RMSE = 0.079, RS = 0.0053, mean square error = 0.0069). Conclusions: The LSTM-RNN model predicts, with the highest accuracy, gender-dependent clinical outcomes in patients with advanced PD after two years of LCIG therapy. Full article

Pancreatic fluid collections (PFCs) are a well-known complication of pancreatitis. PFCs operative management includes percutaneous, endoscopic or surgical drainage. Even if in adult patients, endoscopic drainage is a well-established treatment, few data are available in pediatric setting. We report our single-center experience of EUS-guided cystogastrostomy and lumen-apposing metal stent (LAMS) positioning in children with PFCs; this, at the best of our knowledge, has never been reported before. All consecutive children with PFCs between April 2020 and November 2022 were enrolled in this retrospective study. PFCs were preoperatively evaluated with MRI or CT scan. All the procedures were performed under general anesthesia. A LAMS Hot-Axios^TM 10 × 15 mm was placed in all patients. We evaluated technical feasibility and clinical outcomes, including complications and recurrence rates. Follow-up included clinical observation, blood tests and US. EUS-guided cystogastrostomy was performed in 3 children (2 males; median age 13.2 years). Median maximum cyst diameter was 14.7 cm (range 10–22 cm). Technical and clinical success rates were 100%. No intra or post-operative complications occurred. Our experience suggests that this can be considered a safe and feasible treatment of PCFs even in the pediatric population, as long as the procedure is performed by an expert Endoscopist in a pediatric tertiary-level Center. Full article

Optimizing pain management in spinal surgery is crucial for preventing adverse events due to delayed mobilization. Magnesium sulfate has potential benefits in spinal surgery because of its analgesic properties and modulation of neurotransmitters and autonomic nervous system. Existing evidence regarding the use of magnesium sulfate is partial and controversial, necessitating a comprehensive meta-analysis to evaluate its efficacy and safety. The aim of this study was to conduct a comprehensive meta-analysis to evaluate the efficacy and safety of magnesium sulfate in spinal surgery compared to other available options. This meta-analysis adhered to the PRISMA guidelines. Patients undergoing spinal surgery were included, with the intervention group receiving intravenous magnesium sulfate (MS) at various doses or combinations, whereas the comparison group received other alternatives or a placebo. The efficacy and safety outcomes were assessed. Data were collected from multiple databases and analyzed using Review Manager version 5.4. Heterogeneity was assessed and fixed- or random-effects models were applied. The meta-analysis included eight studies (n = 541). Magnesium sulfate demonstrated significant reductions in pain at 24 h (MD −0.20, 95% CI: −0.39 to −0.02) and opioid consumption (SMD −0.66, 95% CI: −0.95 to −0.38) compared to placebo. Additionally, a decrease in the use of muscle relaxants (SMD −0.91, 95% CI: −1.65 to −0.17) and remifentanil (SMD −1.52, 95% CI: −1.98 to −1.05) was observed. In contrast, an increase in extubation time (MD 2.42, 95% CI: 1.14 to 3.71) and verbal response (MD 1.85, 95% CI: 1.13 to 2.58) was observed compared to dexmedetomidine. In conclusion, magnesium sulfate administration in spinal surgery reduced pain and opioid consumption, and prolonged orientation and verbal response. No significant differences in blood pressure or heart rate were observed between the groups. Full article

This study aimed to investigate prognosis and survival differences in 82 breast cancer patients with germline pathogenic/likely pathogenic variants (PVs) treated and followed at the Breast Unit of the Instituto Nacional de Cancerología, Colombia (INC-C) between 2018 and 2021. Median age at diagnosis was 46 years, with 62.2% presenting locally advanced tumors, 47.6% histological grade 3, and 35.4% with triple-negative breast cancer (TNBC) subtype. Most carriers, 74.4% (61/82), had PVs in known breast cancer susceptibility genes (i.e., “associated gene carriers” group, considered inherited breast cancer cases): BRCA2 (30), BRCA1 (14), BARD1 (4), RAD51D (3), TP53 (2), PALB2 (2), ATM (2), CHEK2 (1), RAD51C (1), NF1 (1), and PTEN (1). BRCA1-2 represented 53.7%, and homologous recombination DNA damage repair (HR-DDR) genes associated with breast cancer risk accounted for 15.9%. Patients with PVs in non-breast-cancer risk genes were combined in a different category (21/82; 25.6%) (i.e., “non-associated gene carriers” group, considered other breast cancer cases). Median follow-up was 38.1 months, and 24% experienced recurrence, with 90% being distant. The 5-year Disease-Free Survival (DFS) for inherited breast cancer cases was 66.5%, and for other breast cancer cases it was 88.2%. In particular, for carriers of PVs in the BRCA2 gene, it was 37.6%. The 5-year Overall Survival (OS) rates ranged from 68.8% for those with PVs in BRCA2 to 100% for those with PVs in other HR-DDR genes. Further studies are crucial for understanding tumor behavior and therapy response differences among Colombian breast cancer patients with germline PVs. Full article

Assessing the regional mass balance of European glaciers presents significant challenges due to limited measurements. While various albedo methods have been explored for individual glaciers, a comprehensive analysis of aggregated albedo methods is lacking. Addressing this gap, in our study, we examined five MODIS aggregated albedos (raw average, minimum average, average minimum, interpolated average, and cumulative) versus the annual mass balance for 12 Austrian and Norwegian glaciers from 2001 to 2020 to establish connections between them. We find that the raw average albedo is strongly correlated with the annual mass balance of Austrian glaciers (r = 0.91), while the interpolated average albedo is significantly correlated with the annual mass balance of Norwegian glaciers (r = 0.90). Moreover, we observe that high-elevation glaciers experience fewer cloud cover days, allowing the raw average albedo to reliably estimate the annual mass balance, whereas low-elevation glaciers are often obscured by clouds, potentially masking the true minimum albedo. Additionally, traditional indicators, such as the equilibrium-line altitude and accumulation area ratio, exhibit significant correlations with the annual mass balance of Norwegian and Austrian glaciers (r = 0.90 and 0.87, respectively), yet albedo demonstrates higher robustness. These findings provide a reference for selecting appropriate aggregation methods to reconstruct glacier mass balance from albedo observations. Full article

Determining whether an audio signal is single compressed (SC) or double compressed (DC) is a crucial task in audio forensics, as it is closely linked to the integrity of the recording. In this paper, we propose the utilization of phase spectrum-based features for detecting DC narrowband and wideband adaptive multi-rate (AMR-NB and AMR-WB) speech. To the best of our knowledge, phase spectrum features have not been previously explored for DC audio detection. In addition to introducing phase spectrum features, we propose a novel parallel LSTM system that simultaneously learns the most representative features from both the magnitude and phase spectrum of the speech signal and integrates both sets of information to further enhance its performance. Analyses demonstrate significant differences between the phase spectra of SC and DC speech signals, suggesting their potential as representative features for DC AMR speech detection. The proposed phase spectrum features are found to perform as well as magnitude spectrum features for the AMR-NB codec, while outperforming the magnitude spectrum in detecting AMR-WB speech. The proposed phase spectrum features yield 8% performance improvement in terms of true positive rate over the magnitude spectrogram features. The proposed parallel LSTM system further improves DC AMR-WB speech detection. Full article

In April 2020, the Aboriginal and Torres Strait Islander COVID-19 Point-of-Care (POC) Testing Program was initiated to improve access to rapid molecular-based SARS-CoV-2 detection in First Nations communities. At capacity, the program reached 105 health services across Australia. An external review estimated the program contributed to averting between 23,000 and 122,000 COVID-19 infections within 40 days of the first infection in a remote community, equating to cost savings of between AU$337 million and AU$1.8 billion. Essential to the quality management of this program, a customised External Quality Assessment (EQA) program was developed with the Royal College of Pathologists of Australasia Quality Assurance Programs (RCPAQAP). From July 2020 to May 2022, SARS-CoV-2 EQA participation ranged from 93 to 100%. Overall concordance of valid EQA results was high (98%), with improved performance following the first survey. These results are consistent with those reported by 12 Australian and 4 New Zealand laboratories for three SARS-CoV-2 RNA EQA surveys in March 2020, demonstrating that SARS-CoV-2 RNA POC testing in primary care settings can be performed to an equivalent laboratory analytical standard. More broadly, this study highlights the value of quality management practices in real-world testing environments and the benefits of ongoing EQA program participation. Full article

The primary nucleation process of α-synuclein (AS) that forms toxic oligomeric species is the early stage of the pathological cause of Parkinson’s disease. It is well-known that copper influences this primary nucleation process. While significant efforts have been made to solve the structures of polymorphic AS fibrils, the structures of AS oligomers and the copper-bound AS oligomers at the molecular level and the effect of copper concentrations on the primary nucleation are elusive. Here, we propose and demonstrate new molecular mechanism pathways of primary nucleation of AS that are tuned by distinct copper concentrations and by a specific copper-binding site. We present the polymorphic AS dimers bound to different copper-binding sites at the atomic resolution in high- and low-copper concentrations, using extensive molecular dynamics simulations. Our results show the complexity of the primary nucleation pathways that rely on the copper concentrations and the copper binding site. From a broader perspective, our study proposes a new strategy to control the primary nucleation of other toxic amyloid oligomers in other neurodegenerative diseases. Full article

In this retrospective study, the relationship between the pN stage of TC and the ultrasound hypoechogenicity of tumour encapsulation and vascular invasion was investigated. The data of a total of 678 TC patients were analysed. The goal of this study was to assess the significance of the pTNM score and preoperative ultrasound features in predicting cancer prognosis and guiding therapeutic decisions in patients with TC. The main research methods included a retrospective analysis of patient data, mainly the pTNM score and presence of tumour encapsulation and vascular invasion obtained from histopathological results and preoperative ultrasound imaging. Patients with well-differentiated TCs (papillary and follicular) were extracted from TC patients to better unify the results because of similar clinical strategies for these TCs. Significant associations were observed between advanced pN stage and the presence of encapsulation and vessel invasion. The majority of pN1a patients exhibited encapsulation (77.71%; p < 0.0001) and vascular invasion (75.30%; p < 0.0001), as did the majority of pN1b patients (100%; p < 0.0001 and 100%; p < 0.0001, respectively). Less than half of the patients with hypoeghogenic patterns presented with encapsulation (43.30%; p < 0.0001) and vascular invasion (43.52%; p < 0.0001), while the vast majority of patients without hypoechogenicity did not present with encapsulation (90.97%; p < 0.0001) or vascular invasion (90.97%; p < 0.0001). Hypoechogenicity was found to be indicative of aggressive tumour behaviour. The results of this study underscore the importance of accurate N staging in TC and suggests the potential use of ultrasound features in predicting tumour behaviour. Further research is needed to confirm these findings and explore additional prognostic markers to streamline TC management strategies and improve patient outcomes. Full article

Hydrogen is an important component of renewable energy and is essential for sustainable development. The cryogenic energy storage system can solve the problem of hydrogen storage. A packed bed can be applied in a cryogenic energy storage system. It is crucial to understand the cryogenic energy discharging in a packed bed. In the present work, the CFD-DEM coupling method is used to investigate the pore-scale flow and heat transfer characteristics of cryogenic hydrogen flowing through the packed bed. To demonstrate the characteristics of the pore-scale heat transfer of the hydrogen flow in a packed bed, the local radial-averaged and axial-averaged temperatures and velocities are analyzed in detail, depending on the local porosity distribution. The pore-scale radial-averaged velocity distribution is proportional to the local radial porosity distribution, whereas the pore-scale radial-averaged temperature characteristics are inverse. Moreover, for the heat exchange of the cryogenic hydrogen flow in a packed bed, it can be found that the cryogenic hydrogen flow is fully heated at an axial distance of approximately 7 dp. Finally, considering that the thermo-physical properties of cryogenic hydrogen are sensitive to the temperature in a packed bed, the friction factor and Nusselt number in the packed bed are also analyzed under various operating parameters, which are in good agreement with certain classic empirical correlations. Full article

As social robots become more prevalent, they often employ non-speech sounds, in addition to other modes of communication, to communicate emotion and intention in an increasingly complex visual and audio environment. These non-speech sounds are usually tailor-made, and research into the generation of non-speech sounds that can convey emotions has been limited. To enable social robots to use a large amount of non-speech sounds in a natural and dynamic way, while expressing a wide range of emotions effectively, this work proposes an automatic method of sound generation using a genetic algorithm, coupled with a random forest model trained on representative non-speech sounds to validate each produced sound’s ability to express emotion. The sounds were tested in an experiment wherein subjects rated the perceived valence and arousal. Statistically significant clusters of sounds in the valence arousal space corresponded to different emotions, showing that the proposed method generates sounds that can readily be used in social robots. Full article

The Internet of Drones (IoD) is a decentralized network linking drones’ access to controlled airspace, providing high adaptability to complex scenarios and services to various drone applications, such as package delivery, traffic surveillance, and rescue, including navigation services. Unmanned Aerial Vehicles (UAVs), combined with IoD principles, offer numerous strengths, e.g., high mobility, wireless coverage areas, and the ability to reach inaccessible locations, including significant improvements such as reliability, connectivity, throughput, and decreased delay. Additionally, emerging blockchain solutions integrated within the concept of the IoD enable effective outcomes that surpass traditional security approaches, while enabling decentralized features for smart human-centered applications. Nevertheless, the combination of the IoD and blockchain faces many challenges with emerging open issues that require further investigation. In this work, we thoroughly survey the technological concept of the IoD and fundamental aspects of blockchain, while investigating its contribution to current IoD practices, the impact of novel enabling technologies, and their active role in the combination of the corresponding synergy. Moreover, we promote the combination of the two technologies by researching their collaborative functionality through different use cases and application fields that implement decentralized IoD solutions and highlighting their indicative benefits, while discussing important challenges and future directions on open issues. Full article

Artificial Intelligence (AI) is redefining electrocardiogram (ECG) analysis in pre-participation examination (PPE) of athletes, enhancing the detection and monitoring of cardiovascular health. Cardiovascular concerns, including sudden cardiac death, pose significant risks during sports activities. Traditional ECG, essential yet limited, often fails to distinguish between benign cardiac adaptations and serious conditions. This narrative review investigates the application of machine learning (ML) and deep learning (DL) in ECG interpretation, aiming to improve the detection of arrhythmias, channelopathies, and hypertrophic cardiomyopathies. A literature review over the past decade, sourcing from PubMed and Google Scholar, highlights the growing adoption of AI in sports medicine for its precision and predictive capabilities. AI algorithms excel at identifying complex cardiac patterns, potentially overlooked by traditional methods, and are increasingly integrated into wearable technologies for continuous monitoring. Overall, by offering a comprehensive overview of current innovations and outlining future advancements, this review supports sports medicine professionals in merging traditional screening methods with state-of-the-art AI technologies. This approach aims to enhance diagnostic accuracy and efficiency in athlete care, promoting early detection and more effective monitoring through AI-enhanced ECG analysis within athlete PPEs. Full article

This study investigated the relationship between subjective grip strength and physical function in community-dwelling older women. Subjective grip strength was assessed using a questionnaire, and physical function and body composition were compared between groups with strong and weak subjective grip strength. Additionally, the two groups were compared in those with mild cognitive impairment (MCI) and those with normal cognitive function, respectively. The results showed significant differences in grip strength (p < 0.001), 30 s chair–stand (CS-30) test (p = 0.039), timed up-and-go (TUG) test (p = 0.027), maximal gait speed (p = 0.029), and skeletal muscle mass (p < 0.001). Older adults with normal cognitive function showed significant differences in grip strength (p < 0.001), quadriceps muscle strength (p < 0.009), one-leg standing time (p = 0.041), CS-30 (p = 0.002), TUG (p = 0.014), gait speed (p = 0.006), and skeletal muscle mass (p = 0.003). Older adults with low subjective grip strength had lower physical function and skeletal muscle mass. However, no items showed significant differences between groups among older adults with MCI. Thus, subjective grip strength is an indicator of an overall decline in physical function and a reduction in skeletal muscle mass in older adults, and cognitive function should be considered when assessing subjective grip strength in older adults. Full article

Streptococcus agalactiae (group B streptococci, GBS) is responsible for severe infections in both neonates and adults. Currently, empiric antimicrobial therapy for sepsis and meningitis is the combined use of penicillin and gentamicin due to the enhanced bactericidal activity. However, high-level gentamicin resistance (HLGR) abrogates the synergism. The rate of HLGR was investigated within a dataset of 433 GBS strains collected from cases of invasive disease in both adults and neonates as well as from pregnant carriers. GBS isolates (n = 20, 4.6%) presented with HLGR (gentamicin MIC breakpoint >1024 mg/L) that was differently diffused between strains from adults or neonates (5.2% vs. 2.8%). Notably, 70% of HLGR GBS strains (14 isolates) were serotype IV. Serotype IV HLGR-GBS isolates were susceptible to all antibiotics tested, exhibited the alpha-C/HvgA/PI-2b virulence string, and belonged to sequence type 1010 (clonal complex (CC) 452). The mobile element that harbored the HLGR aac(6′)-aph(2)″ gene is a novel integrative and conjugative element (ICE) about 45 kb long, derived from GBS 515 ICE tRNA^Lys. The clonal expansion of this HLGR hypervirulent serotype IV GBS CC452 sublineage may pose a threat to the management of infections caused by this strain type. Full article

The assessment of water quality assumes a position of utmost significance as it plays a critical role in upholding ecological balance and safeguarding the well-being of human populations. To achieve these goals, an in-depth consideration of water quality trends is essential, as it offers comprehension into the intricate interplay between various elements within aquatic ecosystems. As a consequence, the proposed work investigates the water quality trends specifically within the Valliyar sub-basin, which encompasses the geographical areas of Kattathurai, Colachal, Thuckalay, and Villukuri. The temporal scope of investigation spans from the year 2000 to 2018 using the dependent variable of water quality parameters with dependent variables of climate data. Recognizing the need for advanced methodologies to tackle the multifaceted nature of water quality dynamics, this research harnesses the power of pioneering machine learning techniques. Two notable approaches, the Radial Bias Function Neural Network (RBFNN) and the DenseNet-121-based Convolutional Neural Network (CNN), are brought into performance. The primary objective is to leverage these techniques to forecast water quality trends for the next twenty-two years. The effectiveness of various machine learning models in predicting water quality is evaluated using the following key performance metrics: the Mean-Square Error (MSE), Mean Absolute Percentage Error (MAPE) and Root-Mean-Square Error (RMSE). Notably, the DenseNet CNN model exhibits accurate prediction among the Artificial Neural Network (ANN), Support Vector Machine (SVM), and Deep Learning (DL) models. This research underscores the significance of machine learning techniques, with DenseNet CNN model emerging as a particularly promising tool in this domain. Full article