Health Concern Research Articles

Data-driven rapid detection of Helicobacter pylori infection through machine learning with limited laboratory parameters in Chinese primary clinics

BackgroundHelicobacter pylori (H. pylori) is a significant global health concern, posing a high risk for gastric cancer. Conventional diagnostic and screening approaches are inaccessible, invasive, inaccurate, time-consuming, and expensive in primary clinics. ObjectiveThis study aims to apply machine learning (ML) models to detect H. pylori infection using limited laboratory parameters from routine blood tests and to investigate the association of these biomarkers with clinical outcomes in primary clinics. MethodsA retrospective analysis with three ML and five ensemble models was conducted on 1409 adults from Hubei Provincial Hospital of Traditional Chinese Medicine. evaluating twenty-three blood test parameters and using the C14 urea breath test as the gold standard for diagnosing H. pylori infection. ResultsIn our comparative study employing three different feature selection strategies, Random Forest (RF) model exhibited superior performance over other ML and ensemble models. Multiple evaluation metrics underscored the optimal performance of the RF model (ROC = 0.951, sensitivity = 0.882, specificity = 0.906, F1 = 0.906, accuracy = 0.894, PPV = 0.908, NPV = 0.880) without feature selection. Key biomarkers identified through importance ranking and shapley additive Explanations (SHAP) analysis using the RF model without feature selection include White Blood Cell Count (WBC), Mean Platelet Volume (MPV), Hemoglobin (Hb), Red Blood Cell Count (RBC), Platelet Crit (PCT), and Platelet Count (PLC). These biomarkers were found to be significantly associated with the presence of H. pylori infection, reflecting the immune response and inflammation levels. ConclusionAbnormalities in key biomarkers could prompt clinical workers to consider H. pylori infection. The RF model effectively identifies H. pylori infection using routine blood tests, offering potential for clinical application in primary clinics. This ML approach can enhance diagnosis and screening, reducing medical burdens and reliance on invasive diagnostics.

Global burden and trends in pre- and post-menopausal gynecological cancer from 1990 to 2019, with Projections to 2040: a cross-sectional study.

The global burden and trends in gynecological cancer (GC) by menopausal status worldwide remain unclear. Data on the number of incident cases and deaths, as well as age-standardized rates (ASR) and risk factors for GC in pre- and post-menopausal women were obtained from the Global Burden of Disease (GBD) Study 2019. The estimated annual percent change was calculated to quantify the temporal trend of GC burden by menopausal status between 1990 and 2019. The Bayesian age-period-cohort model was used to predict the trends in age-standardized incidence and mortality rates for pre- and post-menopausal GC during 2020-2040. In 2019, an estimated 400,146 premenopausal and 879,476 postmenopausal GC cases were newly diagnosed worldwide, with approximately 111,420 and 442,821 GC-related deaths occurring in each menopausal group, respectively. The majority of both pre- and post-menopausal GC cases in low- to middle-SDI regions was due to cervical cancer. In high- and high-middle-SDI regions, premenopausal GC was primarily attributed to cervical cancer, while postmenopausal GC was mainly attributed to uterine cancer. Additionally, the contribution of uterine cancer to GC was higher among postmenopausal women than premenopausal women, across all SDI levels and geographical regions. ASIRs either remained stable or increased from 1990 to 2019 worldwide for both pre- and post-menopausal GC (an average change of 0.03% [95% CI -0.02 to 0.08] and 0.09% [0.05 to 0.13] per year, respectively). However, the age-standardized mortality rates (ASMRs) declined by an annual average of 0.86% (95% CI -0.92 to -0.8) and 0.63% (95% CI -0.66 to -0.6) globally during the same period. The risk-attributable proportion of postmenopausal GC deaths was higher than that of premenopausal GC and increased with increasing SDI. The projections indicate an increasing trend in the burden of premenopausal GC from 2020 to 2040, while the burden of postmenopausal GC is expected to decline. GC continues to be a significant public health concern worldwide, with notable regional and demographic disparities in the burden based on menopausal status. Policymakers and health-care providers must be proactively aware of these evolving trends and tailor age-appropriate and region-specific screening strategies, as well as allocate resources accordingly.

A retrospective study examining the association between polypharmacy and complications after laparoscopic surgery for colorectal cancer

BackgroundPolypharmacy is an escalating public health concern across various healthcare settings worldwide. We aimed to comprehensively investigate postoperative complications after laparoscopic surgery for colorectal cancer and explore their association with polypharmacy. As laparoscopic surgery is widespread, clarifying the association between polypharmacy and postoperative complications is clinically important.MethodsWe retrospectively surveyed the medical charts of adult inpatients who underwent laparoscopic surgery for colorectal cancer at Tohoku Medical and Pharmaceutical University Hospital between April 2019 and March 2023. Postoperative complications were determined using the Clavien–Dindo classification. We explored the factors related to postoperative complications and calculated the cut-off values for the number of medication ingredients.ResultsAmong the 236 patients, 32 (13.6%) developed postoperative complications. On multivariable logistic regression analysis, the number of regularly used medication ingredients (odds ratio = 1.160, 95% confidence interval 1.050–1.270, p = 0.002) was identified as a factor related to postoperative complications. The identified cut-off value for complications was 10 ingredients. Patients using 10 or more ingredients had approximately 3.5 times higher occurrence of postoperative complications than those using fewer than 10 ingredients (33.3% vs. 9.3%, p < 0.001, Fisher’s exact test).ConclusionsOur study comprehensively investigated postoperative complications and examined their association with polypharmacy. We found that the number of regularly used medication ingredients may be linked to complications following laparoscopic surgery for colorectal cancer. These findings have important implications for perioperative management and patient care, providing valuable insights that may influence clinical practices and enhance patient outcomes.

Evaluating the modulation of peripheral immune profile in people living with HIV and (Neuro)cysticercosis.

The parasitic infection caused by Taenia solium represents a significant public health concern in developing countries. Larval invasion of body tissues leads to cysticercosis (CC), while central nervous system (CNS) involvement results in neurocysticercosis (NCC). Both conditions exhibit diverse clinical manifestations, and the potential impact of concomitant HIV infection especially prevalent in sub-Saharan Africa on peripheral and CNS immune responses remains poorly understood. This study aimed to identify the potential impact of HIV coinfection in CC and NCC patients. A nested study within a cross-sectional analysis in two Tanzanian regions was performed and 234 participants (110 HIV+ and 124 HIV-) were tested for cysticercosis antibodies, antigens, CD4 counts and serum Th1 and Th2 cytokines via multiplex bead-based immunoassay. 127 cysticercosis seropositive individuals underwent cranial computed tomography (CCT) and clinical symptoms were assessed. Multiple regression analyses were performed to identify factors associated with cytokine modulation due to HIV in CC and NCC patients. Serologically, 18.8% tested positive for cysticercosis antibodies, with no significant difference HIV+ and HIV+. A significantly higher rate of cysticercosis antigen positivity was found in HIV+ individuals (43.6%) compared to HIV- (28.2%) (p = 0.016). CCT scans revealed that overall 10.3% had active brain cysts (NCC+). Our study found no significant changes in the overall cytokine profiles between HIV+ and HIV- participants coinfected CC and NCC, except for IL-5 which was elevated in HIV+ individuals with cysticercosis. Furthermore, HIV infection in general was associated with increased levels of pro-and some anti-inflammatory cytokines e.g. TNF-α, IL-8, and IFN-γ. However, based on the interaction analyses, no cytokine changes were observed due to HIV in CC or NCC patients. In conclusion, while HIV infection itself significantly modulates levels of key cytokines such as TNF-α, IL-8, and IFN-γ, it does not modulate any cytokine changes due to CC or NCC. This underscores the dominant influence of HIV on the immune system and highlights the importance of effective antiretroviral therapy in managing immune responses in individuals coinfected with HIV and CC/NCC.

Pros and Cons of APOE4 Homozygosity and Effects on Neuroplasticity, Malnutrition, and Infections in Early Life Adversity, Alzheimer's Disease, and Alzheimer's Prevention.

Fortea et al.'s. (2024) recent data analysis elegantly calls attention to familial late-onset Alzheimer's disease (AD) with APOE4 homozygosity. The article by Grant (2024) reviews the factors associated with AD, particularly the APOE genotype and lifestyle, and the broad implications for prevention, both for individuals with the lifestyles associated with living in resource-rich countries and for those enduring environmental adversity in poverty settings, including high exposure to enteric pathogens and precarious access to healthcare. Grant discusses the issue of APOE genotype and its implications for the benefits of lifestyle modifications. This review highlights that bearing APOE4 could constitute an evolutionary benefit in coping with heavy enteric infections and malnutrition early in life in the critical formative first two years of brain development. However, the critical issue may be that this genotype could be a health concern under shifts in lifestyle and unhealthy diets during aging, leading to severe cognitive impairments and increased risk of AD. This commentary supports the discussions of Grant and the benefits of improving lifestyle for decreasing the risks for AD while providing further understanding and modelling of the early life benefits of APOE4 amidst adversity. This attention to the pathophysiology of AD should help further elucidate these critical, newly appreciated pathogenic pathways for developing approaches to the prevention and management in the context of the APOE genetic variations associated with AD.

Intelligent skin lesion segmentation using deformable attention Transformer U-Net with bidirectional attention mechanism in skin cancer images.

In recent years, the increasing prevalence of skin cancers, particularly malignant melanoma, has become a major concern for public health. The development of accurate automated segmentation techniques for skin lesions holds immense potential in alleviating the burden on medical professionals. It is of substantial clinical importance for the early identification and intervention of skin cancer. Nevertheless, the irregular shape, uneven color, and noise interference of the skin lesions have presented significant challenges to the precise segmentation. Therefore, it is crucial to develop a high-precision and intelligent skin lesion segmentation framework for clinical treatment. A precision-driven segmentation model for skin cancer images is proposed based on the Transformer U-Net, called BiADATU-Net, which integrates the deformable attention Transformer and bidirectional attention blocks into the U-Net. The encoder part utilizes deformable attention Transformer with dual attention block, allowing adaptive learning of global and local features. The decoder part incorporates specifically tailored scSE attention modules within skip connection layers to capture image-specific context information for strong feature fusion. Additionally, deformable convolution is aggregated into two different attention blocks to learn irregular lesion features for high-precision prediction. A series of experiments are conducted on four skin cancer image datasets (i.e., ISIC2016, ISIC2017, ISIC2018, and PH2). The findings show that our model exhibits satisfactory segmentation performance, all achieving an accuracy rate of over 96%. Our experiment results validate the proposed BiADATU-Net achieves competitive performance supremacy compared to some state-of-the-art methods. It is potential and valuable in the field of skin lesion segmentation.

Food Neophobia Scores at 8 Years and Associations with Nutrition-related Behaviors at Home in Early Life: Findings from a New Zealand Contemporary Birth Cohort.

Food neophobia (FN) reduces nutritional adequacy and variety which poses a significant concern for children's health and well-being We described the FN scores among 8-year-olds and examined its associations with nutrition-related behaviors at 45 months within the Growing Up in New Zealand cohort (n=4,621). FN was estimated using the Food Neophobia Scale (FNS). Mean FNS scores between variable categories were compared using t-tests for independent samples and ANOVA. Associations between FNS scores at 8 years and nutrition-related behaviors at 45-months were examined using multivariate linear regression. The mean (standard deviation) FN score was 46.2 (15.2) with statistically significant differences by sex (boys=47.6 (15.7), girls=43.8 (14.2), p=<0.001). For all children, in models adjusted by breastfeeding duration and sociodemographic characteristics: children who sometimes and never/almost never the same foods as their parents, scored, on average, 5.8 and 11 points higher in the FNS (versus those who did always/almost always); children who occasionally/never found mealtimes enjoyable scored on average 3.6 points lower in the FNS (versus mostly/quite often); children who always/almost always had the television on during mealtimes scored on average 2.7 higher in the FNS (versus never/almost never). In comparison to children who mostly/quite often had time to talk to others during mealtimes, those who never/occasionally did it scored on average higher points in the FNS overall (1.46 points higher) and within girls (1.73 points higher). These findings support the eating behavior statements in the National Children's Food and Nutrition Guidelines, which emphasize early exposure to food variety, limiting mealtime distractions, and acknowledge that parental role modeling shapes children's nutrition-related behaviors. Early adoption of preventative interventions for reducing FN in early and middle childhood are needed.

Immobilizing DNase in ternary AuAgCu hydrogels to accelerate biofilm disruption for synergistically enhanced therapy of MRSA infections

Bacterial biofilm-related infections have become a significant global concern in public health and economy. Extracellular DNA (eDNA) is regarded as one of the key elements of extracellular polymeric substances (EPS) in bacterial biofilm, providing robust support to maintain the stability of bacterial biofilms for fighting against environmental stresses (such as antibiotics, reactive oxygen species (ROS), and hyperthermia). In this study, ternary AuAgCu hydrogels nanozyme with porous network structures were utilized for the immobilization of DNase (AuAgCu@DNase hydrogels) to realize enhanced biofilm decomposition and antibacterial therapy of MRSA. The prepared AuAgCu@DNase hydrogels can efficiently hydrolyze eDNA in biofilms so that the generated ROS and hyperthermia by laser irradiation can permeate into the interior of the biofilm to achieve deep sterilization. The typical interface interactions between AuAgCu hydrogels and DNase and the excellent photothermal-boost peroxidase-like performances of AuAgCu hydrogels take responsibility for the enhanced antibacterial activity. In the MRSA-infected wounds model, the in vivo antibacterial results revealed that the AuAgCu@DNase hydrogels possess excellent drug-resistant bacteria-killing performance with superb biocompatibility. Meanwhile, the pathological analysis of collagen deposition and fibroblast proliferation of wounds demonstrate highly satisfactory wound healing. This work offers an innovative path for developing nanozyme-enzyme antibacterial composites against drug-resistant bacteria and their biofilms.

The combination of donepezil and cognitive training for improving treatment outcomes for alcohol use disorder: Design of a randomized controlled trial

BackgroundThe development of alcohol use disorder (AUD) is a major concern in public health, and cognitive impairments caused by alcohol are involved in this process. Emerging neurobiological evidence suggests that donepezil, an anticholinesterase agent, may improve AUD treatment outcomes by enhancing neurocognitive functioning. Previous research has also suggested that cognitive remediation therapy (CRT) could potentially improve cognitive function and AUD treatment outcomes. We present the rationale and design of a trial to evaluate the combination of donepezil and cognitive remediation therapy (donepezil + CRT) as an intervention for AUD. MethodsWe propose a 13-week, randomized, double-blind, placebo-controlled, between-subjects trial comparing 4 groups (donepezil + CRT vs. donepezil alone vs. CRT alone vs. placebos) as an intervention for AUD. The main goal of the study is to evaluate if donepezil + CRT is superior to placebo in reducing heavy drinking days and improving neurocognitive functioning. A total of 160 patients (4 groups, 40 per each group) with AUD between the ages of 18–80 years will be recruited at Yale University and the VA Connecticut Healthcare System. Primary outcome measures include 1) heavy drinking by Timeline Follow Back (TLFB) over 13 weeks and 2) global neurocognitive functioning by a global index of neurocognitive function score at 7 and 13 weeks. DiscussionThis protocol paper describes the rationale and proposed methods for the randomized controlled trial for improving AUD treatment outcomes. This project has significant clinical potential to help patients suffering from AUD by improving their cognition and reducing alcohol consumption.Trial Registration:NCT05042102

Assessment of Aflatoxin Contamination in Tanzanian Maize Grains and Soil: Implications for Food Safety and Mitigation Strategies

Maize is a major food security crop contributing over 50% of total food requirement in Tanzania. However, number of factors limit its production and quality. Aflatoxins contamination mostly produced by two Aspergillus species, A. flavus and A. parasticus compromise food safety, public health and economic concern globally. Despite its harmful effects little is documented on the aflatoxin- producing fungi on prevalence’s and contamination levels in soils and maize. The present study investigated Aflatoxigenic occurrence and aflatoxins contamination extents in seven districts. A total of 126 soil and 126 maize samples were collected and analyzed for presence, dominance, and levels of aflatoxin contaminations. Serial dilution and direct plating techniques were used for fungal isolation from soil and maize respectively and macro morphology characterization were used for identification. Aflatoxin levels were detected by High-Performance Liquid Chromatography. A. flavus (38.1%), A. parasiticus (22.2%) and A. niger (16.7%) were found to be the most dominant linked to aflatoxins B1 contamination. Total Aflatoxins were detected in Bahi (72.2%, with mean of 87.9μg/kg), Chemba (61.2%, 74.2μg/kg), and Babati (56.3%, 65.8μg/kg) indicating these were more prone to aflatoxins and higher aflatoxigenic prevalence. Aflatoxin B1 was detected most in Bahi (22.4%, 402.4μg/kg), Chemba (13.4%, 241μg/kg), and Babati (11.1%, 199.2μg/kg). Fungal counts ranged 3.4 ×105 to 6.9 ×107 CFU/g for contaminated samples. Aflatoxigenic species varied significantly (P<0.001) between cropping systems and the locations. Aflatoxigenic occurrence and levels were shown to be influenced by the cropping systems, as evidenced by the soils exhibiting a higher frequency than maize grains. Compared to mixed cropping, the mono-cropping system exhibited the greatest levels of aflatoxigenic population and aflatoxin contamination, indicating that farming practices affect contaminations and the need for improved cropping system practices.

Diagnosis of patients with chronic heart failure implementing wavelet transform and machine learning techniques

Chronic heart failure (CHF) is a significant public health concern due to its increasing prevalence, high number of hospital admissions, and associated mortality. Its prevalence is progressively increasing due to the aging of the population and the decrease in mortality from acute myocardial infarction, among other medical advancements. Consequently, the incidence of CHF predominantly affects older age groups, doubling its prevalence every decade, becoming one of the main causes of mortality in patients older than 65 years. The main objective of this study is to apply machine learning based techniques to determine the best models to classify patients with chronic heart failure through their respiratory pattern. These patterns have been characterized from time series such as inspiratory and expiratory times, breathing duration, and tidal volume obtained from the respiratory flow signal. Based on the behavior of the respiratory pattern, CHF patients were classified into patients with non-periodic breathing, with periodic breathing, and with Cheyene-Stokes respiration (CSR). Time-frequency and statistical techniques have been implemented to analyze these features, and then various classification methods have been applied to define the optimal model with the best accuracy rates. These models could help to better understand the evolution of this disease and in early diagnosis.

Swin transformer-based fork architecture for automated breast tumor classification

Breast cancer constitutes a prevalent and escalating health concern globally. Additionally, the significance of early diagnosis for effective treatment cannot be overstated. Ultrasound imaging (UI) emerges as a cost-effective means for early diagnosis. However, the interpretation of user interface images can be challenging, leading to the development of computer-aided diagnostic systems. In this article, a network architecture named Swin Transformer-based Fork Network (SW-ForkNet) is developed for breast tumor classification. Utilizing the DenseNet121 backbone, SW-ForkNet amalgamates spatial, semantic, and long-context features. The inclusion of the spatial Squeeze-and-Excitation (sSE) block is instrumental for spatial details, while the Swin Transformer structure is employed for capturing global long-context features. Moreover, to optimize the acquisition of these features, a connection is established in the middle layer of the DenseNet121 architecture, feeding both sSE and Swin Transformers. In the network architecture’s output, three distinct feature groups are vectorized, combined, and processed to obtain the final feature map. Finally, a prediction is generated by applying the softmax classifier to this concluding feature map. Experimental evaluations conducted on three datasets (BUSI, GDPH, and SYSUCC) demonstrate the superior performance of SW-ForkNet, showcasing high accuracy (Acc) and F1-Scores (F1S) compared to existing methods and state-of-the-art models. Notable achievements include 93.12 % Acc and 92.27 % F1s for BUSI, 96.15 % Acc and 96.04 % F1S for GDPH, and 94.88 % Acc and 94.03 % F1S for SYSUCC. Consequently, the proposed SW-ForkNet model seems to present itself as a novel and effective structure in breast tumor classification.

Helicobacter pylori: A concise review of the latest treatments against an old foe.

Helicobacter pylori is a significant public health concern given its high prevalence, growing rates of antibiotic resistance, and carcinogenic effect, all of which create management challenges for internists, gastroenterologists, and other specialty physicians. With almost half of the world's human population harboring H pylori, carcinogenic sequelae are a concern to many practitioners. Recent guidelines recommend testing high-risk populations for H pylori using noninvasive or invasive methods. H pylori eradication regimens are tailored based on the presence of effective empiric therapy (local cure rates ≥ 90% for a given regimen) or antimicrobial susceptibility testing. When empiric therapy cure rates are not optimal, guidelines recommend antimicrobial susceptibility testing to improve eradication rates and reduce the progression of antibiotic resistance.

Bridging the Gap: A narrative review of osteoporosis disability, adipokines, and the role of AI in postmenopausal women.

Osteoporosis is a global health concern characterized by reduced bone density and compromised bone quality, resulting in an increased risk of fractures, particularly in postmenopausal women. The assessment of bone mineral density (BMD) plays a pivotal role in diagnosing osteoporosis, as it accounts for approximately 70% of overall bone strength. The World Health Organization (WHO) has endorsed BMD measurement as a reliable method for diagnosing this condition. In Pakistan, the incidence of bone fractures is on the rise, largely attributable to an aging population and a range of contributing factors. Understanding the global and local prevalence of osteoporosis, its impact on morbidity and mortality, and the contributing factors is vital for developing effective preventive and therapeutic strategies. The role of adipokines, including chemerin, vaspin, and omentin-1, in bone metabolism is an emerging area of investigation. These adipokines play diverse roles in physiology, ranging from inflammation and metabolic regulation to cardiovascular health. Understanding their potential impact on bone health is a topic of ongoing research. The intricate relationship between bone density, bone quality, and overall bone strength is central to understanding the diagnosis and management of osteoporosis. Current innovation in machine learning and predictive model can bring revolution in the field of bone health and osteoporosis. Early identification of people with osteoporosis or risk of fracture through machine learning can prevent disability and improve the quality of life.

Anxiolytic and Anticonvulsant Effects of Fisetin Isolated from Bauhinia pentandra on Adult Zebrafish (Danio rerio).

Anxiety and epilepsy are common worldwide and represent a primary global health concern. Fisetin, a flavonoid isolated from Bauhinia pentandra, has a wide range of biological activities may be a promising alternative to combat diseases related to the central nervous system (CNS). The present study aimed to investigate the anxiolytic and anticonvulsant effects of fisetin on adult zebrafish. Furthermore, molecular docking simulations were performed to improve the results. Fisetin did not present toxicity and caused anxiolytic behavior and delayed seizures in animals. This effect may occur through serotonin neurotransmission at 5-HT3A and/or 5-HT3B receptors. Molecular docking simulations showed that fisetin interacts with the orthosteric site of the 5-HT3A receptor with strong H-bond interactions with the Trp156 residue, with a strong contribution from the catechol ring, a behavior similar to that of the antagonist co-crystallized inhibitor granisetron (CWB). Fisetin may be a promising alternative to combat diseases related to the central nervous system. Keywords anxiety • Bauhinia pentandra • Danio rerio • epilepsy • fisetin.

Antimicrobial Stewardship: Smart Approach to Combat Antibiotic Resistance

Abstract: Antimicrobial resistance (AMR) is a major public health concern worldwide. It raises morbidity and mortality and is connected with substantial/significant economic costs. Effective prevention and treatment of an increasing number of diseases caused by pathogens that can no longer be treated with conventional drugs are threatened by antimicrobial re-sistance. AMS programme helps to encourage the appropriate use of antimicrobials. The rise and spread of antibiotic resistance and strategies to combat it are the main topics of this review. Antimicrobial stewardship programme is a healthcare strategy that optimizes the use of anti-biotics and promotes the appropriate use of antimicrobials through the adoption of evidence-based interventions. AMS programme helps to encourage the right use of antimicrobials. This coordinated approach enhances patient outcomes, reduces microbial resistance, and slows the spread of diseases brought on by multidrug-resistant organisms. The growth of antibiotic re-sistance and the strategies for battling it have been the main topics of this review.

Perioperative Naltrexone Management: A Scoping Review by the Perioperative Pain and Addiction Interdisciplinary Network.

Substance use disorders, including alcohol use disorder, are a public health concern that affect more than 150 million people globally. The opioid antagonist naltrexone is being increasingly prescribed to treat opioid use disorder, alcohol use disorder, and chronic pain. Perioperative management of patients on naltrexone is inconsistent and remains a controversial topic, with mismanagement posing a significant risk to the long-term health of these patients. This scoping review was conducted to identify human studies in which the perioperative management of naltrexone was described. This review includes a systematic literature search involving Medline, Medline In-Process, Embase, PsycINFO, and Web of Science. Seventeen articles that describe perioperative naltrexone management strategies were included, including thirteen guidelines, one case report, and three randomized trials. Despite its use in patients with alcohol use disorder and chronic pain, no clinical studies, case reports, or guidelines addressed naltrexone use in these clinical populations. All of the guideline documents recommended the preoperative cessation of naltrexone, irrespective of dose, indication, or route of administration. None of these guideline documents were designed on the basis of a systematic literature search or a Delphi protocol. As described by the primary studies, perioperative pain relief varied depending on naltrexone dose and route of administration, time since last naltrexone administration, and underlying substance use disorder. None of the studies commented on the maintenance of recovery for the patient's substance use disorder in the context of perioperative naltrexone management. The current understanding of the risks and benefits of continuing or stopping naltrexone perioperatively is limited by a lack of high-quality evidence. In patients with risk factors for return to use of opioids or alcohol, the discontinuation of naltrexone should have a strong rationale. Future studies and guidelines should seek to address both acute pain management and maintaining recovery when discussing perioperative naltrexone management strategies.

A comprehensive multi-task deep learning approach for predicting metabolic syndrome with genetic, nutritional, and clinical data

Metabolic syndrome (MetS) is a complex disorder characterized by a cluster of metabolic abnormalities, including abdominal obesity, hypertension, elevated triglycerides, reduced high-density lipoprotein cholesterol, and impaired glucose tolerance. It poses a significant public health concern, as individuals with MetS are at an increased risk of developing cardiovascular diseases and type 2 diabetes. Early and accurate identification of individuals at risk for MetS is essential. Various machine learning approaches have been employed to predict MetS, such as logistic regression, support vector machines, and several boosting techniques. However, these methods use MetS as a binary status and do not consider that MetS comprises five components. Therefore, a method that focuses on these characteristics of MetS is needed. In this study, we propose a multi-task deep learning model designed to predict MetS and its five components simultaneously. The benefit of multi-task learning is that it can manage multiple tasks with a single model, and learning related tasks may enhance the model's predictive performance. To assess the efficacy of our proposed method, we compared its performance with that of several single-task approaches, including logistic regression, support vector machine, CatBoost, LightGBM, XGBoost and one-dimensional convolutional neural network. For the construction of our multi-task deep learning model, we utilized data from the Korean Association Resource (KARE) project, which includes 352,228 single nucleotide polymorphisms (SNPs) from 7729 individuals. We also considered lifestyle, dietary, and socio-economic factors that affect chronic diseases, in addition to genomic data. By evaluating metrics such as accuracy, precision, F1-score, and the area under the receiver operating characteristic curve, we demonstrate that our multi-task learning model surpasses traditional single-task machine learning models in predicting MetS.

Recent advancements in postpartum depression prediction through machine learning approaches: a systematic review

Postpartum depression (PPD) is a significant mental health concern affecting mothers worldwide, irrespective of demographic factors. Detecting and managing PPD at an early stage is crucial for effective intervention. In the context of mental health, intelligent predictive models based on machine learning (ML) have emerged as valuable tools. However, there remains a relative scarcity of research specifically targeting postpartum mental health due to several prominent factors that collectively impede the widespread adoption and practical implementation of ML in the field of PPD. This paper provides an updated overview of ML approaches for PPD prediction. A systematic search across IEEE Xplore, PubMed, Science Direct, and Scopus yielded 1,074 relevant articles. The performance of ML algorithms varies depending on the dataset and the problem being addressed. Notably, the findings reveal that the random forest (RF) algorithm consistently demonstrates the highest predictive accuracy, followed by support vector machine (SVM), logistic regression (LR), XGBoost, and AdaBoost. The development of advanced data techniques in PPD has encouraged interdisciplinary collaboration between researchers in psychiatry and computer science that holds great potential for refining the accuracy and reliability of PPD predictive models, ultimately resulting in improved outcomes for mothers and their families through early detection, intervention, and support.

Inactivation of murine norovirus and hepatitis A virus on various frozen fruits using pulsed light

The frozen fruit sector has experienced significant growth due to improved product quality as well as the advantage of long-term preservation. However, freezing alone does not eliminate foodborne viruses, a major public health concern and considerable economic burden. One promising disinfecting treatment is pulsed light, shown previously to inactivate hepatitis A virus (HAV) and murine norovirus-1 (MNV-1) on the surface of fresh berries. Viral loads were reduced by 1–2 log, with minor visual quality deterioration observed. In this study, an FDA-compliant pulsed light treatment (11.52 J/cm2) was applied to frozen fruits and berries. Infectious MNV-1 and HAV titers were reduced by 1–2 log on most frozen fruits. A noteworthy finding was that reductions of both viruses on cranberries exceeded 3.5 log cycles. Although pulsed light caused a measurable rise in temperature on the product surface, no visible physical changes (e.g., color) were observed, and the fruit pieces were still frozen after treatment. Although the reduction of infectious titer by pulsed light alone was not large (1–2 log), considering the low amount of virus typically found on fruit, it may be beneficial in the frozen fruit sector. It would be easy to combine with other treatments, and synergic interactions might increase virus inactivation.