Index Plots Research Articles

Risk factors for steroid-induced osteonecrosis of the femoral head in children with immune kidney disease

Abstract Objective To investigate the risk factors for steroid-induced osteonecrosis of the femoral head (SONFH) after glucocorticoid (GC) therapy in children with immune kidney diseases. Methods This retrospective study included patients (1–18years) diagnosed with immune kidney disease treated with GCs from January 2012 to July 2022 in our hospital. Data, such as sex, age and body weight at the first GC treatment, and the GC dose, were collected. Patients were divided into SONFH and non-SONFH groups based on the occurrence of SONFH. According to the International Classification of Diseases (ICD-10), the disease diagnosis was searched on the clinical data webpage of our hospital. The descriptive statistics and binary logistic regression analyses were performed. A nomogram was constructed to predict the risk of SONFH in children with immune kidney disease after GC treatment. The predictive ability of the nomogram was evaluated using the concordance index and calibration plots. Results The SONFH and non-SONFH groups included 18 and 90 patients, respectively. Age at the first GC treatment, the six-month cumulative (oral [po.] plus intravenous [iv.]) GC dose, the total cumulative prednisone (po.) dose, total cumulative methylprednisolone (iv.) dose for pulse therapy, and total cumulative GC (po. plus iv.) dose significantly differed between the two groups. Age at the initial GC treatment, the total cumulative prednisone (po.) dose, and the total cumulative methylprednisolone (iv.) dose for pulse therapy were independent risk factors for SONFH in the multivariate analysis. Conclusions This study identified specific risk factors for developing SONFH during GC treatment. Thus, children with immune-related kidney disease undergoing GC treatment should be closely monitored based on these risk factors, which aims to avoid the irreversible damage to the femoral head.

Studying whether the digital economy effectively promotes China’s common prosperity based on the spatial Durbin model

Common prosperity is the essential aspiration of all the Chinese people, and China’s entry into a period of common prosperity coincides with the era of the digital economy. This paper analyzes the impact of the digital economy on the urban-rural income gap from two dimensions industrial digitization and digital industrialization. Based on China’s provincial panel data from 2013 to 2020, the spatial autocorrelation of various indicators in the digital economy is tested employing the global Moran index and local Moran scatter plot. This paper studies whether the digital economy directly affects the income gap between urban and rural areas by constructing a spatial Durbin model with a geographical inverse distance square matrix and whether there is a spatial spillover effect on the income gap between urban and rural areas, to explore how to solve the existing problems in the income gap between urban and rural areas in China through the perspective of digital economy development. The results show that the digital economy in China has a positive spatial autocorrelation to the urban-rural income gap; The impact of the development of the digital economy on the urban-rural income gap is not only reflected in the narrowing of the local region but also through the spatial spillover effect to narrow the income gap in the surrounding region. Digital industrialization has a significant role in narrowing the urban-rural income gap with both direct and indirect effects. Based on the empirical results obtained, this paper proposes that to adjust the imbalance in the development of the digital economy in different regions, digital industrialization and industrial digitalization should be further promoted, and the balanced development of the digital economy should be correctly guided through policy means, to play the regulating role of the digital economy in narrowing the urban-rural income gap.

Development and Validation of a Survival Prediction Model for Patients with Pancreatic Cancer.

Patients with pancreatic ductal adenocarcinoma (PDAC) face challenging treatment decisions following their diagnosis. We developed and validated a survival prognostication model using routinely available clinical information, patient-reported symptoms, performance status, and initial cancer-directed treatment. This retrospective cohort study included PDAC patients from 2007 to 2020 using linked administrative databases in Ontario, Canada. Patients were randomly selected for model development (75%) and validation (25%). Using the development cohort, a multivariable Cox proportional hazards regression with backward stepwise variable selection was used to predict the probability of survival. Model performance was assessed on the validation cohort using the concordance index and calibration plots. There were 17,450 patients (49% female) with a median age of 72 (IQR 63-81) and a mean survival time of nine months. In the derivation cohort, 1,469 (11%) patients had early stage, 4,202 (32%) had advanced stage disease, and 7,417 (57%) had unknown stage. The following factors were associated with an increased risk of death by more than 10%: tumour in the tail of the pancreas, advanced stage, hospitalization three months prior to diagnosis, congestive heart failure or dementia, low, moderate, or high pain score, moderate or high appetite score, high dyspnea and tiredness score, and a performance status score of 60-70 or lower. The calibration plot indicated good agreement with a C index of 0.76. This model accurately predicted one-year survival for PDAC using clinical factors, symptoms, and performance status. This model may foster shared decision making for patients and their providers.

Construction and validation of prognostic models for young cervical cancer patients: age stratification based on restricted cubic splines

Cervical cancer (CC) ranks as the second highest cause of morbidity and mortality among young women; however, there are currently no age-specific definitions for young cervical cancer or prognostic models tailored to this demographic. Data on CC diagnosed between 2000 and 2019 were extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Age stratification is based on the relationship between age and cancer-specific mortality, as demonstrated by restricted cubic spline analyses (RCS). Cox proportional hazards regression analyses were employed to identify independent prognostic factors in the young CC group. Two novel nomograms for this population were developed and validated using an external validation cohort obtained from a local hospital database, evaluated with concordance index (C-index) and calibration plots. Receiver operating characteristic (ROC) curves were utilized to compare the accuracy of the established models against the International Federation of Gynaecology and Obstetrics (FIGO) staging system (2018). A total of 27,658 patients from the SEER database were classified into three age groups (<36 years, 36-60 years, >60 years) based on RCS analyses, with 4,990, 16,922, and 5,746 patients in each group, respectively. The independent prognostic factors identified for young CC included stage, tumour size, grade, histologic type, and surgical intervention. The results of the C-index and calibration in both the training and validation sets confirmed that the two nomograms can accurately predict the occurrence and prognosis of young CC patients. The area under the curve (AUC) values indicated that these models demonstrated higher efficacy in predicting overall survival (OS) compared to the FIGO staging system (2018). These models could potentially serve as effective tools for clinicians to estimate the prognosis of young CC patients.

Cation-lone pair interaction in Alkali/alkaline earth metal ion-heavier borazine analogue complexes.

The present study is the first report on the formation of alkali/alkaline earth metal ion-heavier borazine analogue complexes via cation-lone pair interaction. Density functional calculations are performed in scrutinizing the complex formation between alkali (Li+, Na+, K+)/alkaline earth (Be2+, Mg2+, Ca2+) metal ions and heavier borazine analogues (HBA) viz. B3P3H6, Al3N3H6, Al3P3H6, Al3As3H6, and Ga3P3-H6. The complexes are found to be stable in gas phase with stabilization energies within the range 26.40 to 324.74 kcalmol-1. The stability can be attributed to the polarizing power of the involved metal ions. Presence of solvent phase exerted notable impact on the stability of the complexes; stability is reduced significantly with the increase in solvent polarity. The process of complexation is exothermic and spontaneous. QTAIM analysis indicated the presence of both ionic and covalent interaction between HBAs and metal ions. HOMO energy, Wiberg bond index, NCI-isosurface and RDG plot analysis revealed the major role of cation-lone pair interaction in the complexation process.

Exploring frailty prevalence among adults in Indian healthcare settings: A systematic review and meta-analysis.

Frailty is an age-associated state of increased vulnerability due to declines in physiologic systems, leading to compromised ability to withstand stressors. Given India's rapidly aging population, our study aims to estimate the pooled prevalence of frailty and its associated factors in hospital settings. An extensive search was conducted across four databases, up to January 2024. A random-effects model was utilized. To quantify heterogeneity, the I² statistic, prediction interval, and the Chi-square-based Q test were employed. Outliers were identified using a Baujat plot and influence analysis. Doi plot, luis furuya kanamori (LFK) index and funnel plot were used to assess publication bias. The current meta-analysis determined a pooled frailty prevalence of 42.3% (95%CI: 34.8%-50.1%) and prefrailty prevalence of 39.8% (95%CI: 30.4%-49.8%), both exhibiting high heterogeneity (I² values of 96.9% and 95.3%, respectively). A high degree of variability was indicated by a prediction interval ranging from 9% to 76%, while Egger's test suggested no evidence of publication bias. Our systematic review and meta-analysis, encompassing 6,856 individuals, revealed a considerable prevalence of frailty at 42.3%, underscoring its ubiquity across health spectra and demographics in India.

Geochemical evidences linking the Lower Cretaceous source rocks to the biodegraded bitumen seeps and tar sands in the Gulf of Guinea Basin

The source of biodegraded bitumen seeps and tar sands in the Nigerian axis of the Gulf of Guinea province has been a contentious issue for decades. The most prevalent opinion is that the biodegraded bitumen seeps are migration oils that were degraded by bacterial. In the present study, outcrop source rocks (Lower Cretaceous), core samples (Upper Cretaceous) from a shallow well-x, and bitumen seeps and tar sands from the eastern axis of the Dahomey Basin of Nigeria were analyzed. Rock-Eval™ pyrolysis show that the outcrop rocks are classified as having excellent-very good source quality; in contrast, those from well-x are classified as having only poor-fair quality. The cross plot of hydrogen index (HI) against Tmax indicates that samples from the outcrops contain type II/III organic matter, whereas well samples contain type III organic matter and all source rocks are thermally immature, which is supported by vitrinite reflectance (Ro: 0.20–0.48 %), and biomarker maturity parameters (C32 (S/S + R), MPI-I). In the bitumen seeps and tar sands, isoprenoids, n-alkanes, steranes and hopanes have been degraded, and tricyclic and tetracyclic terpanes are still mostly intact. In addition, C30 hopane and homohopanes have been demethylated, and C25 norhopanes are present in abundance, suggesting that the bitumen seeps and tar sands experienced heavy biodegradation in the range of PM-6 or greater. The distribution of 4,9-DMD, 4,8-DMD and 3,4-DMD diamondoids and the δ13C of saturate and aromatic fractions indicate that the bitumen seeps and tar sands were generated from Types II and III source rocks. The ratio of demethylated Ts/Tm indicates that the samples are low maturity and were generated from immature source rocks. Cross plots of tricyclic terpane ratios (C25/C26TT vs C25TT/C24TeT, C23/C21TT vs (C19+20)/C23TT, and C24TeT/C24TeT + C26TT vs (C19+20)/C23TT) also reveal that the biodegraded bitumen seeps and tar sands are closely related to source rocks in outcrop groups 1 and 2. This conclusion is supported by the ternary plots of tricyclic terpanes, 4,9DMD, 4,8DMD and 3,4DMD diamondoids, and the cross plot of EAI against DMDI-1. The occurrence of exsudatinite in the outcrop source rocks in group 1, consistent with early hydrocarbon generation, and the low ratio of demethylated Ts/Tm (<1) in the bitumen samples confirms that the biodegraded bitumen seeps and tar sands in the Nigerian Dahomey Basin were generated by the Lower Cretaceous source rocks in group 1. The study advances on the previous propositions on the source of bitumen seeps and tar sands and contributes our knowledge in the Gulf of Guinea Basin.

The impact of hepatitis B surface antigen seroconversion on the durability of functional cure induced by pegylated interferon alpha treatment

BackgroundHepatitis B surface antigen (HBsAg) loss is regarded as a pivotal criterion for assessing functional cure in patients diagnosed chronic hepatitis B (CHB). We conducted the research to investigate the real-world performance of HBsAg seroconversion in sustaining HBsAg loss.MethodsThis retrospective analysis confirmed 295 patients who attained HBsAg loss through combination therapy involving nucleos(t)ide analogues (NAs) and pegylated interferon alpha (peg-IFNα). Employing Kaplan–Meier estimates method to conduct survival analysis. The forest plot was used to visualize the results of multivariate Cox regression, and selected variables were included in the nomogram.ResultsHBsAg seroreversion was observed in 45 patients during follow-up periods, with a lower recurrence risk in patients with HBsAg seroconversion at the end of peg-IFNα therapy (EOT) (10.3% vs 37.3% at 96-week, P < 0.0001). Moreover, the sustainability of hepatitis B surface antibody (anti-HBs) in participants continuing therapy after HBsAg seroconversion was superior to those discontinued prematurely (72.5% vs 54.5% at 96 weeks, P = 0.012). Additionally, the former group was also relatively less likely to experience HBsAg reversion during long-term observation (8.4% vs 14.3% at 96 weeks, P = 0.280). Hepatitis B envelope antigen (HBeAg) status, anti-HBs status and consolidation treatment screened by multivariable analysis were utilized to construct a predictive model for HBsAg reversion. The concordance index(C-index = 0.77) and calibration plots indicated satisfactory discrimination and consistency of nomogram.ConclusionsHBsAg seroconversion was beneficial for sustaining functional cure in patients treated with peg-IFNα. Continuing consolidation therapy after HBsAg seroconversion also contributed to maintain HBsAg seroconversion and improve the durability of HBsAg loss. The nomogram illustrated its efficacy as a valuable instrument in showcasing survival probability of functional cure.

Nomogram for predicting 10-year postoperative recurrence of stage I gastric cancer.

With the advancement of various auxiliary examination techniques, the detection rate of stage I gastric cancer has gradually increased, and its clinical first-choice treatment is surgery. Although patients with stage I gastric cancer generally have a good postoperative survival rate, there is still a certain probability of recurrence. Given the large number of gastric cancer cases, there is a vast population of patients with stage I disease. We are aiming to identify the risk factors for postoperative recurrence of stage I gastric cancer and to establish a reliable predictive model to assess the risk of recurrence in the population for clinical practice. In this retrospective cohort study, we utilized the Surveillance, Epidemiology, and End Results (SEER) database to investigate predictive factors for recurrence among stage I gastric cancer patients who underwent curative gastrectomy between 2000 and 2018. The cohort was divided into training and validation sets for the development and validation of a nomogram. Prognostic factors were evaluated through univariate and multivariate Cox regression analyses. Significant variables identified by the concordance index (C-index) and calibration plots were used to construct nomograms predicting the probability of 5- and 10-year recurrence. Risk factors for recurrence included sex, age, race, histology, tumor size, American Joint Committee on Cancer Tumor (AJCC T) and primary site, which were used to construct the nomogram. The C-index for both the training and validation cohorts indicated that the nomogram possessed good calibration and discrimination abilities in predicting the probability of 5- and 10-year recurrence after curative surgery for stage I gastric cancer. This study established a reliable predictive model for recurrence following curative gastrectomy in stage I gastric cancer based on a population cohort. The findings of this study have the potential to significantly impact clinical practice by providing clinicians with tools for personalized risk assessment and for making informed treatment decisions.

Inhibition of Epithelial-Mesenchymal Transition and Migration in Cisplatin-resistant Cancer Through Combined Treatment With Cisplatin and SH003.

This study investigated the synergistic effects of combining cisplatin and SH003 treatment on the viability, apoptosis, cytotoxicity, migration and epithelial-mesenchymal transition (EMT) in cisplatin-resistant cancer cell lines YD-8/CIS, YD-9/CIS and YD-38/CIS. The 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay was used to assess cell viability, while trypan blue exclusion assay was used to evaluate cytotoxicity. Flow cytometry and western blot analysis measured apoptotic cell death. Wound-healing assays evaluated cell migration and EMT markers. Combination index (CI) plots were used to evaluate the combinatory effects of the treatment. Combination therapy significantly reduced cell viability more effectively than each agent alone, as demonstrated by the MTT assay, with CI plots confirming notable synergism. Trypan blue exclusion assays indicated increased cell death and cytotoxicity in combination treatment than in both monotherapies, although the increase was not significant. Flow cytometry and western blot analysis revealed no significant synergistic effect on apoptotic cell death. However, wound-healing assays revealed that the combination of cisplatin and SH003 significantly inhibited cell migration and regulated EMT markers, indicating the potential reversal of EMT. Combining cisplatin and SH003 therapy may potentially be a more effective strategy for treating cisplatin-resistant cancer by increasing cytotoxicity and inhibiting metastasis. Further research is required to elucidate the underlying mechanisms and evaluate the in vivo efficacy of this combination therapy.

Noninvasive Method Using Mechanical Extensometer for the Estimation of Intracranial Compliance by Repeated Measures Agreement Analysis.

Intracranial compliance refers to the relationship between changes in volume and the resultant changes in intracranial pressure (ICP). This study aimed to assess the agreement of a noninvasive ICP waveform device for the estimation of compliance compared with invasive ICP monitoring employing three distinct methods. We conducted a retrospective analysis of ICP waveform morphology recorded through both invasive (external ventricular drain) and noninvasive (mechanical extensometer) methods in adult patients with acute brain injury admitted to a neurointensive care unit between August 2021 and August 2022. Compliance was calculated as the amplitude of the fundamental component of cerebral arterial blood volume (estimated with concurrent Transcranial Doppler [TCD] recordings), divided by the amplitude of the fundamental component of the invasive and noninvasive ICP waveforms. Subsequently, we assessed the agreement between invasive and noninvasive intracranial compliance by repeated measures correlation coefficient analysis using three methods: TCD-derived, P2/P1 ratio, and time-to-peak (TTP). A linear mixed-effects model was used to compute the concordance correlation coefficient, total deviation index, and coefficient of individual agreement. Coverage probability plot was calculated to estimate the percent of observations within different cut points for each of the three methods. A total of 21 patients were identified for this study. Repeated measures correlation analysis showed a strong correlation (R = 0.982, 95% confidence interval [0.980-0.984], p < 0.0001) between log-transformed noninvasive and invasive compliance. Agreement statistics for TCD, P2/P1 ratio, and TTP indicated that although the concordance correlation coefficient was highest for log(TCD) values, TTP and P2:P1 ratio measures had better agreement with total deviation index and coverage probability plot analyses. Repeated measures correlations suggest that ICP waveform analyses may offer a more accurate estimate of compliance than TCD-derived methods for noninvasive ICP monitoring. Further validations studies are warranted to confidently establish this method as an indicator of intracranial compliance.

Defining the exit meta-analysis.

In recent decades, clinical research has seen significant advancements, both in the generation and synthesis of evidence through meta-analyses. Despite these methodological advancements, there is a growing concern about the accumulation of repetitive and redundant literature, potentially contributing to research waste. This highlights the necessity for a mechanism to determine when a meta-analysis has conclusively addressed a research question, signaling no further need for additional studies-a concept we term an "exit" meta-analysis. We introduced a convergence index, the Doi-Abdulmajeed Trial Stability (DAts) index, and a convergence plot to determine the exit status of a meta-analysis. The performance of DAts was examined through simulation and applied to two real-world meta-analyses. The DAts index and convergence plot demonstrate highly effective discriminative ability across varying study scenarios. This represents the first attempt to define an exit meta-analysis using a quantitative measurement of stability (as opposed to sufficiency) and its corresponding plot. The application to real-world scenarios further validated the utility of DAts and the convergence plot in identifying a conclusive (exit) meta-analyses. The new development of DAts and the convergence plot provide a promising tool for investigating the conclusiveness of meta-analyses. By identifying an exit status for meta-analysis, the scientific community may be equipped to make better-informed decisions on the continuation of research on a specific topic, thereby preventing research waste and focusing efforts on areas with unresolved questions.

Carbon dioxide storage and cumulative oil production predictions in unconventional reservoirs applying optimized machine-learning models

To achieve carbon dioxide (CO2) storage through enhanced oil recovery, accurate forecasting of CO2 subsurface storage and cumulative oil production is essential. This study develops hybrid predictive models for the determination of CO2 storage mass and cumulative oil production in unconventional reservoirs. It does so with two multi-layer perceptron neural networks (MLPNN) and a least-squares support vector machine (LSSVM), hybridized with grey wolf optimization (GWO) and/or particle swarm optimization (PSO). Large, simulated datasets were divided into training (70%) and testing (30%) groups, with normalization applied to both groups. Mahalanobis distance identifies/eliminates outliers in the training subset only. A non-dominated sorting genetic algorithm (NSGA-II) combined with LSSVM selected seven influential features from the nine available input parameters: reservoir depth, porosity, permeability, thickness, bottom-hole pressure, area, CO2 injection rate, residual oil saturation to gas flooding, and residual oil saturation to water flooding. Predictive models were developed and tested, with performance evaluated with an overfitting index (OFI), scoring analysis, and partial dependence plots (PDP), during training and independent testing to enhance model focus and effectiveness. The LSSVM-GWO model generated the lowest root mean square error (RMSE) values (0.4052 MMT for CO2 storage and 9.7392 MMbbl for cumulative oil production) in the training group. That trained model also exhibited excellent generalization and minimal overfitting when applied to the testing group (RMSE of 0.6224 MMT for CO2 storage and 12.5143 MMbbl for cumulative oil production). PDP analysis revealed that the input features “area” and “porosity” had the most influence on the LSSVM-GWO model's prediction performance. This paper presents a new hybrid modeling approach that achieves accurate forecasting of CO2 subsurface storage and cumulative oil production. It also establishes a new standard for such forecasting, which can lead to the development of more effective and sustainable solutions for oil recovery.

Validation of the Danish Version of the Overall Depression Severity and Impairment Scale (ODSIS): A Reliable Instrument for Assessing Depression Symptoms and Functional Impairment.

In psychotherapy, having a reliable tracking tool is vital for effective treatment. We have validated the Danish version of the 'Overall Depression Severity and Impairment Scale' (ODSIS), which is used in the Unified Protocol for Emotional Disorders (UP) and other cognitive behavioural therapies. This five-item self-rating scale is expected to correspond closely to the gold-standard Beck Depression Inventory-II (BDI-II), but also to self-rating scales addressing daily functioning and well-being. Internal consistency is assessed using Cronbach's alpha and McDonald's omega. Criterion validity is explored through Pearson's correlation coefficient with BDI-II, the Work and Social Adjustment Scale (WSAS), the Five-Item World Health Organization Well-Being Index (WHO-5) and scatter plots, and an optimal cut-off score is determined using a receiver operating characteristic curve. The study included 340 respondents: 234 from a recent clinical trial, 56 psychiatric patients from inpatient/outpatient units and 50 healthy volunteers. ODSIS demonstrates strong internal consistency (Cronbach's alpha: 0.887-0.956, McDonald's omega: 0.888-0.957). Correlations with BDI-II, WSAS and WHO-5 highlight its strong criterion validity (BDI-II: 0.847, WSAS: 0.751, WHO-5: 0.771). The optimal cut-off score (> 7.5) for detecting depression shows great sensitivity (0.862) and specificity (0.731). The Danish version of ODSIS proves to be a reliable instrument for quantification of a combination of depression level and functional impairment from the symptoms. Its high correlation with established BDI-II makes it a brief and handy tool for repeated measurement in clinical and research settings.

A novel prediction model for the probability of aggressive behavior in patients with mood disorders: Based on a cohort study

BackgroundAccurately predicting the probability of aggressive behavior is crucial for guiding early intervention in patients with mood disorders. MethodsCox stepwise regression was conducted to identify potential influencing factors. Nomogram prediction models were constructed to predict the probabilities of aggressive behavior in patients with mood disorders, and their performance was assessed using consistency index (C-index) and calibration plots. ResultsResearch findings on 321 patients with mood disorders indicated that being older (HR = 0.92, 95% CI: 0.86–0.98), single (HR = 0.11, 95% CI: 0.02–0.68), having children (one child, HR = 0.07, 95%CI: 0.01–0.87; more than one child, HR = 0.33, 95%CI: 0.04–2.48), living in dormitory (HR = 0.25, 95%CI: 0.08–0.77), non-student (employee, HR = 0.24, 95% CI: 0.07–0.88; non-employee, HR = 0.09, 95% CI: 0.02–0.35), and higher scores in subjective support (HR = 0.90, 95% CI: 0.82–0.99) were protective factors. On the contrary, minorities (HR = 5.26, 95% CI: 1.23–22.48), living alone (HR = 4.37, 95% CI: 1.60–11.94), having suicide history (HR = 2.51, 95% CI: 1.06–5.95), and having higher scores in EPQ-E (HR = 1.04, 95% CI: 1.00–1.08) and EPQ-P (HR = 1.03, 95% CI: 1.00–1.07) were identified as independent risk factors for aggressive behavior in patients with mood disorders. The nomogram prediction model demonstrated high discrimination and goodness-of-fit. ConclusionsA novel nomogram prediction model for the probability of aggressive behavior in patients with mood disorders was developed, effective in identifying at-risk populations and offering valuable insights for early intervention and proactive measures.

Unexpected Ag-H bond in the predictive silver hydride phase under high pressure

Silver, typically acknowledged for its inertness toward hydrogen under standard conditions, exhibits a noteworthy shift in reactivity under elevated pressures. Employing first-principles variable-composition evolutionary search calculations, our investigation unveils the presence of thermodynamically stable Ag-H compounds. A novel stoichiometry, Ag2H, characterized by P3¯m1 symmetry, emerges as the thermodynamically stable phase at 100 GPa. Notably, the trigonal-Ag2H phase at 100 GPa demonstrates both mechanical and dynamic stability, as ascertained through the computation of elastic constants and phonon dispersions, respectively. Examination of the electronic band structure and density of states manifests the metallic nature of the resulting Ag2H compound. A comprehensive analysis involving crystal orbital bond index, Bader charge, and charge density plots substantiates the ionic character of Ag2H, attributable to effective charge transfer from silver atoms to hydrogen. Furthermore, application of the Allen-Dynes modified McMillan equation indicates the absence of superconducting behavior in Ag-H systems at the elevated pressure of 100 GPa.

Seasonal and Time-Series Analysis of Road Traffic Accidents

Road Traffic Accidents (RTAs) contribute a significant and escalating rate in harm, hospitalization, and mortality. This study assesses the seasonal trend and time series analysis of RTAs cases brought to casualty department of Liaquat University Hospital, Hyderabad, Pakistan. Objective: To conduct seasonal and time series analysis of RTA cases at Casualty Department of Liaquat University Hospital Hyderabad. Methods: Nine hundred and sixty-six road traffic accidents-related cases were examined through prospective study at casualty department, Liaquat University Hospital, Hyderabad, Pakistan from January 2020 to December 2022. The seasonal trend was demonstrated by drawing seasonal index plot and time series plot. Results: The largest occurrences of RTA-related cases were reported in the month of December, having 103 total cases with a mean of 34.33 ± 18.92. July had the second highest frequency of RTAs-related cases, with a total of 94 cases having a mean of 31 ± 5.43 cases. The second-highest number of cases was observed in July and November with approximately equal mean number of RTAs. The time series analysis demonstrated an increasing trend of RTAs-related cases in the last three months of the year. Conclusions: The number of cases connected to RTAs increased during seasons of extreme weather, such as the summer and winter, and it also showed an increasing tendency in the last quarter of 2022.

Nomograms based on ratio indexes to predict severity and prognosis in immune checkpoint inhibitors-related myocarditis: a retrospective analysis

PurposeImmune checkpoint inhibitors-related myocarditis (ICI-M) is one of the immune-related adverse events (irAEs), which is rare and highly lethal. This study aimed to establish nomograms based on ratio biomarkers to predict the severity and prognosis of ICI-M.MethodsWe retrospectively examined patients with advanced cancers who were also diagnosed with ICI-M at the Fourth Hospital of Hebei Medical University. The patients of ICI-M were divided into mild and severe groups and a 40-day following up was carried out. The major adverse cardiovascular events(MACEs) were regarded as the endpoint. Nomogram-based models were established and validated.ResultsSeventy-seven patients were involved, including 31 severe cases(40.3%). Lactate dehydrogenase-to-albumin ratio(LAR) combined with the change rate from baseline to onset of LAR(▵\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ riangle$$\\end{document}LAR) which performed best to diagnose the severe ICI-M was identified to establish the nomogram-based model. The bootstrap-corrected concordance index [0.752 95% confidence interval (CI): 0.635-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}0.866] and calibration plot with good degree of fitting confirmed this diagnostic model. Neutrophil-to-high-density lipoprotein cholesterol ratio(NHR) and LAR were also screened into the nomogram-based model for 40-day MACEs after ICI-M, which performed well by validating for concordance index(0.779 95% CI: 0.677-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$-$$\\end{document}0.865)and calibration plots after being bootstrap-corrected. Moreover, a ≥\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge$$\\end{document}101% increase in LAR significantly separated patients in MACE-free survival.ConclusionRatio indexes at onset and their change rates from baseline showed good diagnostic value for the severity of ICI-M and prognostic value for subsequent MACEs, particularly LAR, NHR and their change rates. The nomogram-based models of ratio indexes could provide a potential choice for early detection and monitor of the severe ICI-M and subsequent MACEs.Graphical abstract

Organic petrographic and mineralogical composition of the No. 6 coal seam of the Soutpansberg Coalfield, South Africa: Insights into paleovegetation and depositional environment

This study investigates the paleodepositional conditions of the No. 6 Seam of the Madzaringwe Formation in Makhado and Voorburg south area of the Soutpansberg Coalfield (Limpopo Province, South Africa) utilizing organic petrography and inorganic geochemical proxies. The coals are predominantly high-volatile bituminous B-A rank with high ash yields (avg. 36.1 wt%), characterized by high-vitrinite (~ 41.5 vol%), moderate-to-high inertinite (9.8 vol%–33.7 vol%) and low liptinite (~ 2.3 vol%). The distribution of inertinite varies among different coal horizons (from bottom-lower to middle-upper), suggesting differential oxidation conditions and/or paleofire occurrence. Vitrinite-to-inertinite (V/I) ratio, tissue preservation–gelification index (TPI–GI), and groundwater–vegetation index (GWI–VI) plots, indicate that the peat-forming forest-swamp vegetation accumulated under mesotrophic-to-rheotrophic hydrological conditions. The presence of structured macerals (i.e., telinite, collotelinite, fusinite, and semifusinite) suggests well-preserved plant tissues, whereas framboidal pyrite and sulphur content (0.24 wt%–2.16 wt%) point to brackish-water influence at the peat stage. The coals contain quartz, kaolinite, siderite, muscovite, dolomite, calcite, and pyrite minerals, most of which were likely sourced from felsic igneous rocks. The Al/(Al+Fe+Mn) and (Fe+Mn)/Ti ratios for the studied samples range between 0.24–0.97 and 0.57–70.10, respectively. The ratios, Al–Fe–Mn plot, and presence of massive botryoidal-type pyrite imply some influence of meteoric waters or fluids from hydrothermal activity post-deposition. Moreover, the chemical index of alteration (CIA: 98.25–99.67), chemical index of weathering (CIW: 92.04–97.66), and A–CN–K ternary diagram suggest inorganic matter suffered strong chemical weathering, indicating warm paleoclimatic conditions during the coal formation.

Bivariate extreme value analysis of extreme temperature and mortality in Canada, 2000-2020.

Climate change increases the risk of illness through rising temperature, severe precipitation and worst air pollution. This paper investigates how monthly excess mortality rate is associated with the increasing frequency and severity of extreme temperature in Canada during 2000-2020. The extreme associations were compared among four age groups across five sub-blocks of Canada based on the datasets of monthly T90 and T10, the two most representative indices of severe weather monitoring measures developed by the actuarial associations in Canada and US. We utilize a combined seasonal Auto-regressive Integrated Moving Average (ARIMA) and bivariate Peaks-Over-Threshold (POT) method to investigate the extreme association via the extreme tail index and Pickands dependence function plots. It turns out that it is likely (more than 10%) to occur with excess mortality if there are unusual low temperature with extreme intensity (all except Northeast Atlantic (NEA), Northern Plains (NPL) and Northwest Pacific (NWP) for age group 0-44), while extreme frequent high temperature seems not to affect health significantly (all except NWP). Particular attention should be paid to NWP and Central Arctic (CAR) since population health therein is highly associated with both extreme frequent high and low temperatures (both for all age groups). The revealed extreme dependence is expected to help stakeholders avoid significant ramifications with targeted health protection strategies from unexpected consequences of extreme weather events. The novel extremal dependence methodology is promisingly applied in further studies of the interplay between extreme meteorological exposures, social-economic factors and health outcomes.