Predicting peak inundation depths with a physics informed machine learning model.

Timely, accurate, and reliable information is essential for decision-makers, emergency managers, and infrastructure operators during flood events. This study demonstrates that a proposed machine learning model, MaxFloodCast, trained on physics-based hydrodynamic simulations in Harris County, offers efficient and interpretable flood inundation depth predictions. Achieving an average R 2 of 0.949 and a Root Mean Square Error of 0.61 ft (0.19 m) on unseen data, it proves reliable in forecasting peak flood inundation depths. Validated against Hurricane Harvey and Tropical Storm Imelda, MaxFloodCast shows the potential in supporting near-time floodplain management and emergency operations. The model's interpretability aids decision-makers in offering critical information to inform flood mitigation strategies, to prioritize areas with critical facilities and to examine how rainfall in other watersheds influences flood exposure in one area. The MaxFloodCast model enables accurate and interpretable inundation depth predictions while significantly reducing computational time, thereby supporting emergency response efforts and flood risk management more effectively.

Challenges and complications and their management of the transarterial microembolization for chronic musculoskeletal pain.

Transarterial microembolization (TAME) is an increasingly well-known novel and minimally invasive treatment option for painful chronic musculoskeletal diseases that is gaining popularity. Although the safety and effectiveness of TAME have been established, limited knowledge of intraarticular and musculocutaneous arterial anatomy may lead to challenges and complications. This article aims to present cases illustrating these challenges and complications, based on multicenter experiences and a comprehensive literature review. Furthermore, the article also provides preventive tips, solutions, and follow-up strategies to reduce the learning curve for interventional radiologists and facilitate familiarity with post-TAME follow-up images for diagnostic radiologists. CLINICAL RELEVANCE STATEMENT: This article illustrates the intra- and post-interventional complications of transarterial microembolization (TAME) through detailed pictorial reviews, including how to distinguish them from normal angiographic findings. It provides strategies for their prevention, management, and follow-up, which can further improve clinical outcomes. KEY POINTS: • Transarterial microembolization for chronic musculoskeletal pain may result in intrainterventional challenges (IIC) and postinterventional complications (PIC), and their importance may be underestimated. • The intrainterventional challenges include microarterial perforation, arterial dissection, and catheter tip fracture, whereas postinterventional complications include tissue ischemia-related complications, puncture site hemorrhage, and arterial injury. • Being familiar with the intrainterventional challenges and postinterventional complications may help minimize the procedure risk and improve outcomes.

Quality of Stormwater Infrastructure Systems in Vulnerable Communities: Three Case Studies from Texas.

A properly functioning local stormwater drainage system is essential for mitigating flood risks. This study evaluates the quality of roadside drainage channels in three underserved communities in Texas: the Sunnyside neighborhood in Houston (Harris County), a neighborhood in the City of Rockport (Aransas County), and the Hoehn colonia (Hidalgo County). These communities have a history of flooding, are highly socially vulnerable, and rely on roadside ditches as their principal stormwater drainage system for runoff control. Mobile lidar (Light Detection and Ranging) measurements were collected for 6.09 miles of roadside channels in these communities. The raw lidar measurements were processed to evaluate drainage conditions based on the channel's geometric properties, hydraulic capacity, and level of service. The assessment results are linked to a Geographic Information System (GIS) tool for enhanced visualization. Finally, the paper provides insights regarding the quality of stormwater infrastructure in the study communities and discusses their practical implications.

Survival Benefit of Experience of Liver Resection for Advanced Recurrent Hepatocellular Carcinoma Treated with Sorafenib: A Propensity Score Matching Analysis.

Several studies have shown that liver resection (LR) confers better survival outcomes in intermediate- and advanced-stage hepatocellular carcinoma (HCC) patients. However, the postoperative recurrence rate is high, and little is known about the survival benefits of LR for recurrent HCC patients who have already received systemic treatment. This study aimed to evaluate the impact of LR on recurrent advanced-stage HCC patients who received sorafenib as a systemic treatment. In this study, 147 advanced HCC patients were enrolled between 1 January 2012 and 31 December 2019. Two study groups were classified, based on whether they underwent LR or not. To reduce the possible selection bias, a propensity score matching (PSM) analysis was performed. The primary study endpoint was set as overall survival (OS), and the secondary endpoint was set as progression-free survival (PFS). Our study results revealed that advanced HCC patients who received sorafenib with LR had a longer OS than did those without LR, whether before or after PSM (15.0 months vs. 6.0 months, HR 0.45, 95% CI 0.31-0.67, p < 0.001; 15.0 months vs. 5.0 months, HR 0.46, 95% CI 0.28-0.76, p = 0.001). Similar results were obtained in PFS, before or after PSM (4.14 months vs. 2.60 months, HR 0.60, 95% CI 0.40-0.89, p = 0.01; 4.57 months vs. 2.63 months, HR 0.58, 95% CI 0.34-0.97, p = 0.037). Multivariate analysis showed that the experience of LR was independent of other factors associated with better OS and PFS, whether before or after PSM (p < 0.05). Therefore, advanced HCC patients who have undergone liver resection should be encouraged to continue sorafenib treatment to improve prognosis.

Community-scale big data reveals disparate impacts of the Texas winter storm of 2021 and its managed power outage.

Aggregated community-scale data could be harnessed to provide insights into the disparate impacts of managed power outages, burst pipes, and food inaccessibility during extreme weather events. During the winter storm that brought historically low temperatures, snow, and ice to the entire state of Texas in February 2021, Texas power-generating plant operators resorted to rolling blackouts to prevent collapse of the power grid when power demand overwhelmed supply. To reveal the disparate impact of managed power outages on vulnerable subpopulations in Harris County, Texas, which encompasses the city of Houston, we collected and analyzed community-scale big data using statistical and trend classification analyses. The results highlight the spatial and temporal patterns of impacts on vulnerable subpopulations in Harris County. The findings show a significant disparity in the extent and duration of power outages experienced by low-income and minority groups, suggesting the existence of inequality in the management and implementation of the power outage. Also, the extent of burst pipes and disrupted food access, as a proxy for storm impact, were more severe for low-income and minority groups. Insights provided by the results could form a basis from which infrastructure operators might enhance social equality during managed service disruptions in such events. The results and findings demonstrate the value of community-scale big data sources for rapid impact assessment in the aftermath of extreme weather events.

Specifying evacuation return and home-switch stability during short-term disaster recovery using location-based data.

The objectives of this study are: (1) to specify evacuation return and home-switch stability as two critical milestones of short-term recovery during and in the aftermath of disasters; and (2) to understand the disparities among subpopulations in the duration of these critical recovery milestones. Using privacy-preserving fine-resolution location-based data, we examine evacuation and home move-out rates in Harris County, Texas in the context of the 2017 Hurricane Harvey. For each of the two critical recovery milestones, the results reveal the areas with short- and long-return durations and enable evaluating disparities in evacuation return and home-switch stability patterns. In fact, a shorter duration of critical recovery milestone indicators in flooded areas is not necessarily a positive indication. Shorter evacuation return could be due to barriers to evacuation and shorter home move-out rate return for lower-income residents is associated with living in rental homes. In addition, skewed and non-uniform recovery patterns for both the evacuation return and home-switch stability were observed in all subpopulation groups. All return patterns show a two-phase return progress pattern. The findings could inform disaster managers and public officials to perform recovery monitoring and resource allocation in a more proactive, data-driven, and equitable manner.

Using IVIM Parameters to Differentiate Prostate Cancer and Contralateral Normal Tissue through Fusion of MRI Images with Whole-Mount Pathology Specimen Images by Control Point Registration Method.

The intravoxel incoherent motion (IVIM) model may enhance the clinical value of multiparametric magnetic resonance imaging (mpMRI) in the detection of prostate cancer (PCa). However, while past IVIM modeling studies have shown promise, they have also reported inconsistent results and limitations, underscoring the need to further enhance the accuracy of IVIM modeling for PCa detection. Therefore, this study utilized the control point registration toolbox function in MATLAB to fuse T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) MRI images with whole-mount pathology specimen images in order to eliminate potential bias in IVIM calculations. Sixteen PCa patients underwent prostate MRI scans before undergoing radical prostatectomies. The image fusion method was then applied in calculating the patients' IVIM parameters. Furthermore, MRI scans were also performed on 22 healthy young volunteers in order to evaluate the changes in IVIM parameters with aging. Among the full study cohort, the f parameter was significantly increased with age, while the D* parameter was significantly decreased. Among the PCa patients, the D and ADC parameters could differentiate PCa tissue from contralateral normal tissue, while the f and D* parameters could not. The presented image fusion method also provided improved precision when comparing regions of interest side by side. However, further studies with more standardized methods are needed to further clarify the benefits of the presented approach and the different IVIM parameters in PCa characterization.

Correction: Visual light perceptions caused by medical linear accelerator: Findings of machine-learning algorithms in a prospective questionnaire-based case-control study.

[This corrects the article DOI: 10.1371/journal.pone.0247597.].

A Novel Graph Neural Network Methodology to Investigate Dihydroorotate Dehydrogenase Inhibitors in Small Cell Lung Cancer.

Small cell lung cancer (SCLC) is a particularly aggressive tumor subtype, and dihydroorotate dehydrogenase (DHODH) has been demonstrated to be a therapeutic target for SCLC. Network pharmacology analysis and virtual screening were utilized to find out related proteins and investigate candidates with high docking capacity to multiple targets. Graph neural networks (GNNs) and machine learning were used to build reliable predicted models. We proposed a novel concept of multi-GNNs, and then built three multi-GNN models called GIAN, GIAT, and SGCA, which achieved satisfactory results in our dataset containing 532 molecules with all R^2 values greater than 0.92 on the training set and higher than 0.8 on the test set. Compared with machine learning algorithms, random forest (RF), and support vector regression (SVR), multi-GNNs had a better modeling effect and higher precision. Furthermore, the long-time 300 ns molecular dynamics simulation verified the stability of the protein-ligand complexes. The result showed that ZINC8577218, ZINC95618747, and ZINC4261765 might be the potentially potent inhibitors for DHODH. Multi-GNNs show great performance in practice, making them a promising field for future research. We therefore suggest that this novel concept of multi-GNNs is a promising protocol for drug discovery.

Visual light perceptions caused by medical linear accelerator: Findings of machine-learning algorithms in a prospective questionnaire-based case-control study.

This study aimed to investigate the possible incidence of visual light perceptions (VLPs) during radiation therapy (RT). We analyzed whether VLPs could be affected by differences in the radiation energy, prescription doses, age, sex, or RT locations, and whether all VLPs were caused by radiation. From November 2016 to August 2018, a total of 101 patients who underwent head-and-neck or brain RT were screened. After receiving RT, questionnaires were completed, and the subjects were interviewed. Random forests (RF), a tree-based machine learning algorithm, and logistic regression (LR) analyses were compared by the area under the curve (AUC), and the algorithm that achieved the highest AUC was selected. The dataset sample was based on treatment with non-human units, and a total of 293 treatment fields from 78 patients were analyzed. VLPs were detected only in 122 of the 293 exposure portals (40.16%). The dataset was randomly divided into 80% and 20% as the training set and test set, respectively. In the test set, RF achieved an AUC of 0.888, whereas LR achieved an AUC of 0.773. In this study, the retina fraction dose was the most important continuous variable and had a positive effect on VLP. Age was the most important categorical variable. In conclusion, the visual light perception phenomenon by the human body during RT is induced by radiation rather than being a self-suggested hallucination or induced by phosphenes.

Adaptation of Medical Students During Clinical Training: Effects of Holistic Preclinical Education on Clerkship Performance.

THEORY: Taiwan's medical undergraduate program at a university or medical center is a continuation of 12 years of compulsory citizenship education rooted in holistic philosophies. Students acquire both technical knowledge and nontechnical attributes, which are necessary for success in further work and life. The early clinical learning experiences of medical students are primarily acquired through clerkships. These clerkships require medical students to apply and extend what they learned during their preclinical education; however, previous studies have explored this issue through examining fragmentary factors such as preclinical course grades and traits but not undertaking comprehensive, whole-person investigations. HYPOTHESES: To account for the potential benefits of a holistic approach in medical students' learning, we propose three hypotheses: Medical students' preclinical performance on Taiwan's technical and nontechnical higher education assessments are positively associated with their clinical competence (Hypothesis 1) and psychological well-being (Hypothesis 2) during clerkships, and medical students' psychological well-being during clerkships is positively associated with their clinical competence (Hypothesis 3). METHOD: We studied a cohort of 65 medical students engaged in clerkships from September 2013 to April 2015. Their preclinical technical knowledge scores-formal curricular grades received from course instructors-were obtained from their medical school's archival dataset. Their nontechnical attributes-moral and social performance scores received from student mentors and physical performance scores received from course instructors-were also obtained from the school's archival data set. The medical students' competence in their 2-year clinical clerkships was measured using the objective structured clinical examination scores from the end of both clerkship years. The medical students' psychological well-being during their 2-year clerkships was measured according to burnout level, which was determined using routine online surveys that employed validated, structured, and self-administered questionnaires at each specialty rotation. Multiple regressions and linear mixed-effects model were employed for statistical analysis. RESULTS: Our study revealed that higher preclinical technical knowledge predicted superior clinical competence and a higher level of burnout during clerkships. By contrast, higher preclinical nontechnical attributes (i.e., higher preclinical moral, social, and physical performance) predicted lower level of burnout. However, no relationship was discovered between clerkship burnout and the clinical competence of the medical students. CONCLUSIONS: Our study verified the value of a holistic education that encompasses both technical knowledge and nontechnical attributes during the preclinical learning stage for medical students. Our findings can serve as a reference for medical educators designing preclinical educational programs for medical students.

Matrix metalloproteinase 14 modulates diabetes and Alzheimer's disease cross-talk: a meta-analysis.

Diabetes mellitus is associated with dementia, but whether diabetes is associated with Alzheimer's disease remains controversial. Alzheimer's disease is characterized by amyloid beta aggregation. We hypothesized that genes, involved in amyloid beta degradation, may be altered due to diabetes and thus participate in progression of Alzheimer's disease. Expression profiling of amyloid beta-degrading enzymes in streptozotocin-induced diabetic mice and their correlation with expression of amyloid precursor protein in hippocampus of Alzheimer's disease patients were accessed. We found that matrix metalloproteinase 14 decreased in brain but not in other tissues of streptozotocin-induced diabetic mice, and was negatively correlated with expression of amyloid precursor protein in hippocampus of Alzheimer's disease patients. These findings suggested matrix metalloproteinase 14 may link insulin-deficient diabetes to Alzheimer's disease.

Network pharmacology-based approach of novel traditional Chinese medicine formula for treatment of acute skin inflammation in silico.

Matrix metalloproteinase-9 (MMP-9) appears to play an important role in acute skin inflammation. Subantimicrobial dose of tetracycline has been demonstrated to inhibit the activity of MMP-9 protein. However, long-term use tetracycline will induce side effect. The catalytic site of MMP-9 is located at zinc-binding amino acids, His401, His405 and His411. We attempted to search novel medicine formula as MMP-9 inhibitors from traditional Chinese medicine (TCM) database by using in silico studies. We utilized high-throughput virtual screening to find which natural compounds could bind to the zinc-binding site. The quantitative structure-activity relationship (QSAR) models, which constructed by scaffold of MMP-9 inhibitors and its activities, were employed to predict the bio-activity of the natural compounds for MMP-9. The results showed that Celacinnine, Lobelanidine and Celallocinnine were qualified to interact with zinc-binding site and displayed well predictive activity. We found that celallocinnine was the best TCM compound for zinc binging sites of MMP-9 because the stable interactions were observed under dynamic condition. In addition, Celacinnine and Lobelanidine could interact with MMP-9 related protein that identified by drug-target interaction network analysis. Thus, we suggested the herbs Hypericum patulum, Sedum acre, and Tripterygium wilfordii that containing Celallocinnine, Celacinnine and Lobelanidine might be a novel medicine formula to avoid the side effect of tetracycline and increase the efficacy of treatment.

Residue-based design of small molecule inhibitor for H1N1, H5N1 and H7N1 mutants.

Point mutations H274Y and N294S can lead to resistance of influenza virus strains to some drug molecules. Recently, a large number of experiments has focused on the many frameworks and catalytic residues thought to prevent the efficacy of anti-flu drugs. In the past, most research has considered the role of drugs in rigid proteins rather than in flexible proteins. In this study, we used molecular dynamics simulation (MD) combined with structure- and ligand-based drug design (SBDD and LBDD) methods to study dynamic interaction and protein dynamics correlation statistics between compounds and both the framework and catalytic residues in influenza virus N1 strains. Drug candidates were screened using the IC50 of the docking result predicted by support vector machine, multiple linear regression, and genetic function approximation (P < 0.001). As shown by MD, saussureamine C and diiodotyrosine have a protein dynamics correlation similar to that of sialic acid, and both can participate in hydrogen bond formation with loop, framework, and catalytic residues. Our in silico findings suggest that saussureamine C can inhibit H274Y and N294S mutants, and that diiodotyrosine can also inhibit N294S mutants. Therefore, the drugs saussureamine C and diiodotyrosine have the potential to produce inhibitory effects on wild-type influenza virus and some N1 mutants.

Redesigning a clinical mentoring program for improved outcomes in the clinical training of clerks.

INTRODUCTION: Mentorship has been noted as critical to medical students adapting to clinical training in the medical workplace. A lack of infrastructure in a mentoring program might deter relationship building between mentors and mentees. This study assessed the effect of a redesigned clinical mentoring program from the perspective of clerks. The objective was to assess the benefits of the redesigned program and identify potential improvements. METHODS: A redesigned clinical mentoring program was launched in a medical center according to previous theoretical and practical studies on clinical training workplaces, including the elements of mentor qualifications, positive and active enhancers for mentor-mentee relationship building, the timing of mentoring performance evaluation, and financial and professional incentives. A four-wave web survey was conducted, comprising one evaluation of the former mentoring program and three evaluations of the redesigned clinical mentoring program. Sixty-four fifth-year medical students in clerkships who responded to the first wave and to at least two of the three following waves were included in the study. A structured and validated questionnaire encompassing 15 items on mentor performance and the personal characteristics of the clerks was used. Mixed linear models were developed for repeated measurements and to adjust for personal characteristics. RESULTS: The results revealed that the redesigned mentoring program improved the mentors' performance over time for most evaluated items regarding professional development and personal support provided to the mentees. CONCLUSIONS: Our findings serve as an improved framework for the role of the institution and demonstrate how institutional policies, programs, and structures can shape a clinical mentoring program. We recommend the adoption of mentorship schemes for other cohorts of medical students and for different learning and training stages involved in becoming a physician.

Medical students' awareness and perception of national health examinations.

Key ingredients for upgrading health care include bolstering and appraising professional medical education. Health examination as a crucial element of health care that we must incorporate into medical education. This research evaluates medical students' awareness of national health examinations. Two surveys, focused on health examination knowledge and perspective, were conducted for first- to fourthyear medical students, results analyzed by descriptive statistics, t-test and ANOVA. Research subjects scored maximum 11 (of possible 15): i.e., 76.2% accuracy for health examination knowledge questions and held positive views on seven (58%) perspective-related questions. Self-directed learning courses do provide a positive effect on students' learning. Respondents' varying backgrounds had insignificant impact on overall results, but in-depth analysis for each individual question does reveal differences among several backgrounds. Medical students' overall awareness level for health examination is above average in comparison to the general public. This research result can provide a basis to improve the related professional programs, courses and teachings or used as a reference for modifications on future classes. The above observations were discussed based on the medical education system in Taiwan.

Lead discovery for Alzheimer's disease related target protein RbAp48 from traditional Chinese medicine.

Deficiency or loss of function of Retinoblastoma-associated proteins (RbAp48) is related with Alzheimer's disease (AD), and AD disease is associated with age-related memory loss. During normal function, RbAp48 forms a complex with the peptide FOG-1 (friend of GATA-1) and has a role in gene transcription, but an unstable complex may affect the function of RbAp48. This study utilizes the world's largest traditional Chinese medicine (TCM) database and virtual screening to provide potential compounds for RbAp48 binding. A molecular dynamics (MD) simulation was employed to understand the variations after protein-ligand interaction. FOG1 was found to exhibit low stability after RbAp48 binding; the peptide displayed significant movement from the initial docking position, a phenomenon which matched the docking results. The protein structure of the other TCM candidates was not variable during MD simulation and had a greater stable affinity for RbAp48 binding than FOG1. Our results reveal that the protein structure does not affect ligand binding, and the top three TCM candidates Bittersweet alkaloid II, Eicosandioic acid, and Perivine might resolve the instability of the RbAp48-FOG1 complex and thus be used in AD therapy.

Potential smoothened inhibitor from traditional Chinese medicine against the disease of diabetes, obesity, and cancer.

Nowadays, obesity becomes a serious global problem, which can induce a series of diseases such as type 2 diabetes mellitus, cancer, cardiovascular disease, metabolic syndrome, and stoke. For the mechanisms of diseases, the hedgehog signaling pathway plays an important role in body patterning during embryogenesis. For this reason, smoothened homologue (Smo) protein had been indicated as the drug target. In addition, the small-molecule Smo inhibitor had also been used in oncology clinical trials. To improve drug development of TCM compounds, we aim to investigate the potent lead compounds as Smo inhibitor from the TCM compounds in TCM Database@Taiwan. The top three TCM compounds, precatorine, labiatic acid, and 2,2'-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid), have displayed higher potent binding affinities than the positive control, LY2940680, in the docking simulation. After MD simulations, which can optimize the result of docking simulation and validate the stability of H-bonds between each ligand and Smo protein under dynamic conditions, top three TCM compounds maintain most of interactions with Smo protein, which keep the ligand binding stable in the binding domain. Hence, we propose precatorine, labiatic acid, and 2,2'-[benzene-1,4-diylbis(methanediyloxybenzene-4,1-diyl)]bis(oxoacetic acid) as potential lead compounds for further study in drug development process with the Smo protein.