Did the COVID-19 quarantine policies applied in Cochabamba, Bolivia mitigated cases successfully? an interrupted time series analysis.

BACKGROUND: The COVID-19 pandemic prompted varied policy responses globally, with Latin America facing unique challenges. A detailed examination of these policies' impacts on health systems is crucial, particularly in Bolivia, where information about policy implementation and outcomes is limited. OBJECTIVE: To describe the COVID-19 testing trends and evaluate the effects of quarantine measures on these trends in Cochabamba, Bolivia. METHODS: Utilizing COVID-19 testing data from the Cochabamba Department Health Service for the 2020-2022 period. Stratified testing rates in the health system sectors were first estimated followed by an interrupted time series analysis using a quasi-Poisson regression model for assessing the quarantine effects on the mitigation of cases during surge periods. RESULTS: The public sector reported the larger percentage of tests (65%), followed by the private sector (23%) with almost double as many tests as the public-social security sector (11%). In the time series analysis, a correlation between the implementation of quarantine policies and a decrease in the slope of positive rates of COVID-19 cases was observed compared to periods without or with reduced quarantine policies. CONCLUSION: This research underscores the local health system disparities and the effectiveness of stringent quarantine measures in curbing COVID-19 transmission in the Cochabamba region. The findings stress the importance of the measures' intensity and duration, providing valuable lessons for Bolivia and beyond. As the global community learns from the pandemic, these insights are critical for shaping resilient and effective health policy responses.

Main findings: The findings highlight the importance of stringent quarantine measures in managing infectious disease outbreaks, offering valuable insights for policymakers worldwide in strategizing effective public health interventions.Added knowledge: By providing a detailed analysis of testing disparities and quarantine policies' effectiveness within a specific Latin American context, our research fills a critical gap in understanding their impacts on health system responses and disease control.Global health impact for policy and action: The findings highlight the importance of stringent quarantine measures in managing infectious disease outbreaks, offering valuable insights for policymakers worldwide in strategizing effective public health interventions.

Multi-step ahead forecasting of electrical conductivity in rivers by using a hybrid Convolutional Neural Network-Long Short-Term Memory (CNN-LSTM) model enhanced by Boruta-XGBoost feature selection algorithm.

Electrical conductivity (EC) is widely recognized as one of the most essential water quality metrics for predicting salinity and mineralization. In the current research, the EC of two Australian rivers (Albert River and Barratta Creek) was forecasted for up to 10 days using a novel deep learning algorithm (Convolutional Neural Network combined with Long Short-Term Memory Model, CNN-LSTM). The Boruta-XGBoost feature selection method was used to determine the significant inputs (time series lagged data) to the model. To compare the performance of Boruta-XGB-CNN-LSTM models, three machine learning approaches-multi-layer perceptron neural network (MLP), K-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) were used. Different statistical metrics, such as correlation coefficient (R), root mean square error (RMSE), and mean absolute percentage error, were used to assess the models' performance. From 10 years of data in both rivers, 7 years (2012-2018) were used as a training set, and 3 years (2019-2021) were used for testing the models. Application of the Boruta-XGB-CNN-LSTM model in forecasting one day ahead of EC showed that in both stations, Boruta-XGB-CNN-LSTM can forecast the EC parameter better than other machine learning models for the test dataset (R = 0.9429, RMSE = 45.6896, MAPE = 5.9749 for Albert River, and R = 0.9215, RMSE = 43.8315, MAPE = 7.6029 for Barratta Creek). Considering the better performance of the Boruta-XGB-CNN-LSTM model in both rivers, this model was used to forecast 3-10 days ahead of EC. The results showed that the Boruta-XGB-CNN-LSTM model is very capable of forecasting the EC for the next 10 days. The results showed that by increasing the forecasting horizon from 3 to 10 days, the performance of the Boruta-XGB-CNN-LSTM model slightly decreased. The results of this study show that the Boruta-XGB-CNN-LSTM model can be used as a good soft computing method for accurately predicting how the EC will change in rivers.

Temporal dynamics of depressive symptoms and cognitive decline in the oldest old: dynamic time warp analysis of the Leiden 85-plus study.

BACKGROUND: The prevalence of depressive symptoms and cognitive decline increases with age. We investigated their temporal dynamics in individuals aged 85 and older across a 5-year follow-up period. METHODS: Participants were selected from the Leiden 85-plus study and were eligible if at least three follow-up measurements were available (325 of 599 participants). Depressive symptoms were assessed at baseline and at yearly assessments during a follow-up period of up to 5 years, using the 15-item Geriatric Depression Scale (GDS-15). Cognitive decline was measured through various tests, including the Mini Mental State Exam, Stroop test, Letter Digit Coding test and immediate and delayed recall. A novel method, dynamic time warping analysis, was employed to model their temporal dynamics within individuals, in undirected and directed time-lag analyses, to ascertain whether depressive symptoms precede cognitive decline in group-level aggregated results or vice versa. RESULTS: The 325 participants were all 85 years of age at baseline; 68% were female, and 45% received intermediate to higher education. Depressive symptoms and cognitive functioning significantly covaried in time, and directed analyses showed that depressive symptoms preceded most of the constituents of cognitive impairment in the oldest old. Of the GDS-15 symptoms, those with the strongest outstrength, indicating changes in these symptoms preceded subsequent changes in other symptoms, were worthlessness, hopelessness, low happiness, dropping activities/interests, and low satisfaction with life (all P's < 0.01). CONCLUSION: Depressive symptoms preceded cognitive impairment in a population based sample of the oldest old.

A causal machine-learning framework for studying policy impact on air pollution: A case-study in COVID-19 lockdowns.

When studying the impact of policy interventions or natural experiments on air pollution, such as new environmental policies and opening or closing an industrial facility, careful statistical analysis is needed to separate causal changes from other confounding factors. Using COVID-19 lockdowns as a case-study, we present a comprehensive framework for estimating and validating causal changes from such perturbations. We propose using flexible machine learning-based comparative interrupted time series (CITS) models for estimating such a causal effect. We outline the assumptions required to identify causal effects, showing that many common methods rely on strong assumptions that are relaxed by machine learning models. For empirical validation, we also propose a simple diagnostic criterion, guarding against false effects in baseline years when there was no intervention. The framework is applied to study the impact of COVID-19 lockdowns on NO2 in the eastern US. The machine learning approaches guard against false effects better than common methods and suggest decreases in NO2 in Boston, New York City, Baltimore, and Washington D.C. The study showcases the importance of our validation framework in selecting a suitable method and the utility of a machine learning based CITS model for studying causal changes in air pollution time series.

Leveraging VQ-VAE tokenization for autoregressive modeling of medical time series.

In this work, we present CodeAR, a medical time series generative model for electronic health record (EHR) synthesis. CodeAR employs autoregressive modeling on discrete tokens obtained using a vector quantized-variational autoencoder (VQ-VAE), which addresses key challenges of accurate distribution modeling and patient privacy preservation in the medical domain. The proposed model is trained with next-token prediction instead of a regression problem for more accurate distribution modeling, where the autoregressive property of CodeAR is useful to capture the inherent causality in time series data. In addition, the compressive property of the VQ-VAE prevents CodeAR from memorizing the original training data, which ensures patient privacy. Experimental results demonstrate that CodeAR outperforms the baseline autoregressive-based and GAN-based models in terms of maximum mean discrepancy (MMD) and Train on Synthetic, Test on Real tests. Our results highlight the effectiveness of autoregressive modeling on discrete tokens, the utility of CodeAR in causal modeling, and its robustness against data memorization.

The Role of Robotics in Meeting Institutional Goals: A Unified Strategy to Facilitate Program Excellence.

PURPOSE: The rapid expansion of robotic surgical equipment necessitates a review of the needs and challenges faced by hospitals introducing robots for the first time to compete with experienced institutions. The aim of this study was to analyze the impact of robotic surgery on our hospital compared to open and laparoscopic surgery, examine internal transformations, and assess regional, domestic, and international implications. METHODS: A retrospective review was conducted of electronic medical records (EMRs) from 2019 to 2022 at Inha University Hospital, including patients who underwent common robotic procedures and equivalent open and laparoscopic operations. The study investigated clinical and operational performance changes in the hospital after the introduction of robotic technology. It also evaluated the operational effectiveness of robot implementation in local, national, and international contexts. To facilitate comparison with other hospitals, the data were transmitted to Intuitive Surgical, Inc. for analysis. The study was conducted in compliance with domestic personal information regulations and received approval from our Institutional Review Board. RESULTS: We analyzed EMR data from 3,147 patients who underwent surgical treatment. Over a period of 3.5 years, the adoption of robotic technology in a hospital setting significantly enhanced the technical skills of all professors involved. The introduction of robotic systems led to increased patient utilization of conventional surgical techniques, as well as a rise in the number of patients choosing robotic surgery. This collective trend contributed to an overall increase in patient numbers. This favorable evaluation of the operational effectiveness of our hospital's robot implementation in the context of local, national, and global factors is expected to positively influence policy changes. CONCLUSION: Stakeholders should embrace data science and evidence-based techniques to generate valuable insights from objective data, assess the health of robot-assisted surgery programs, and identify opportunities for improvement and excellence.

Autotransplantation with Adjunctive Application of Enamel Matrix Derivatives Using a Digital Workflow: A Prospective Case Series.

OBJECTIVES: Digital protocols and bioactive materials may reduce complications and improve tooth autotransplantation (ATT) success and survival rates. This prospective study assesses the performance of a fully digital autotransplantation protocol of close-apex molars with the adjunctive application of Enamel Matrix Derivatives (EMD). METHODS: Twelve adult patients with 13 hopeless molar teeth were replaced with autotransplantation of closed apex third molars. Outcomes, including success and survival rates, clinical, endodontic, radiographic, patient-reported outcome measures (PROMs), and digital image assessments, were conducted over a two-year follow-up period. RESULTS: Survival and success rates were 100% and 91.2%, respectively, with no progressive inflammatory or replacement root resorption (ankylosis) except for one tooth presenting radiographic furcation involvement. A significant probing depth reduction of 2.4 ± 2.58 mm and CAL gains of 2.8 ± 3.03 mm were observed in transplanted teeth compared to the hopeless receptor teeth. Radiographic bone levels remained stable throughout the study period (-0.37 ± 0.66 mm), and digital image assessments showed minimal alveolar ridge width changes (-0.32 to -0.7 mm) and gingival margin changes (-0.95 to -1.27 mm) from baseline to last visit. PROMs indicated very high patient satisfaction. CONCLUSION: The use of a digital ATT protocol with adjunctive use of EMD in closed-apex third molars demonstrated promising short-term high success and survival rates. Additionally, this type of therapy adequately preserves the dimensions of the alveolar ridge in the receptor site.

Multi level perspectives in stock price forecasting: ICE2DE-MDL.

This study proposes a novel hybrid model, called ICE2DE-MDL, integrating secondary decomposition, entropy, machine and deep learning methods to predict a stock closing price. In this context, first of all, the noise contained in the financial time series was eliminated. A denoising method, which utilizes entropy and the two-level ICEEMDAN methodology, is suggested to achieve this. Subsequently, we applied many deep learning and machine learning methods, including long-short term memory (LSTM), LSTM-BN, gated recurrent unit (GRU), and SVR, to the IMFs obtained from the decomposition, classifying them as noiseless. Afterward, the best training method was determined for each IMF. Finally, the proposed model's forecast was obtained by hierarchically combining the prediction results of each IMF. The ICE2DE-MDL model was applied to eight stock market indices and three stock data sets, and the next day's closing price of these stock items was predicted. The results indicate that RMSE values ranged from 0.031 to 0.244, MAE values ranged from 0.026 to 0.144, MAPE values ranged from 0.128 to 0.594, and R-squared values ranged from 0.905 to 0.998 for stock indices and stock forecasts. Furthermore, comparisons were made with various hybrid models proposed within the scope of stock forecasting to evaluate the performance of the ICE2DE-MDL model. Upon comparison, The ICE2DE-MDL model demonstrated superior performance relative to existing models in the literature for both forecasting stock market indices and individual stocks. Additionally, to our knowledge, this study is the first to effectively eliminate noise in stock item data using the concepts of entropy and ICEEMDAN. It is also the second study to apply ICEEMDAN to a financial time series prediction problem.

Hybrid convergent procedure with proactive oesophageal cooling for the treatment of long-standing persistent atrial fibrillation: a case series.

Background: The hybrid convergent procedure is approved to treat symptomatic patients with long-standing persistent atrial fibrillation (AF). Despite direct visualization during surgical ablation as well as the use of luminal oesophageal temperature (LET) monitoring, oesophageal injury is still possible. A dedicated device for proactive oesophageal cooling has recently been cleared by the Food and Drug Administration to reduce the likelihood of ablation-related oesophageal injury resulting from radiofrequency cardiac ablation procedures. This report describes the first uses of proactive oesophageal cooling for oesophageal protection during the epicardial ablation portion of hybrid convergent procedures. Case summary: Five patients with long-standing persistent AF underwent hybrid convergent ablations with the use of proactive oesophageal cooling as means of oesophageal protection. All cases were completed successfully with no adverse effects. Most notably, cases were shorter when compared to cases using LET monitoring, likely due to lack of pauses for overheating of the oesophagus that would otherwise be required to prevent damage to the oesophagus. Discussion: This report describes the first uses of proactive oesophageal cooling for oesophageal protection during the epicardial ablation portion of five hybrid convergent procedures. Use of cooling enabled uninhibited deployment of lesions without the need to pause energy delivery due to elevated temperatures in the oesophagus, providing a feasible alternative to LET monitoring.

IgG4-related disease with nasopharyngeal malignancy-like manifestations.

Background: IgG4-related disease (IgG4-RD) was characterized by single or multiple masses in organs, which may mimic various inflammatory and malignant diseases. Here, we summarize 4 patients with aggressive manifestations of IgG4-RD that mimic nasopharynx cancer to provide some new sights for the diagnosis of IgG4-RD. Case summary: Four patients were included in our series. The age ranged from 53 to 64 years old, and the duration of the disease ranged from 4 to 6 months. The chief complaints included headache, rhinorrhea, or diplopia. All patients had more than 10 IgG4+ plasma cells/HPF in immunohistochemistry with plasma lgG4 levels ranging from 218 mg/dL to 765 mg/dL. All of them met the diagnostic criteria of lgG4-RD. Conclusion: The described case is highly similar to the clinical manifestations of nasopharyngeal carcinoma. Although pathology is the gold standard, there are still limitations. Serological IgG4 can help confirm the diagnosis. Timely diagnosis of IgG4-RD is of great significance in preventing secondary organ damage in patients with active diseases.

Melanin-based color variation in response to changing climates in snakes.

Melanism, the process of heavier melanin deposition, can interact with climate variation at both micro and macro scales, ultimately influencing color evolution in organisms. While the ecological processes regulating melanin production in relation to climate have been extensively studied, intraspecific variations of melanism are seldom considered. Such scientific gap hampers our understanding of how species adapt to rapidly changing climates. For example, dark coloration may lead to higher heat absorption and be advantageous in cool climates, but also in hot environments as a UV or antimicrobial protection mechanism. To disentangle such opposing predictions, here we examined the effect of climate on shaping melanism variation in 150 barred grass snakes (Natrix helvetica) and 383 green whip snakes (Hierophis viridiflavus) across Italy. By utilizing melanistic morphs (charcoal and picturata in N. helvetica, charcoal and abundistic in H. viridiflavus) and compiling observations from 2002 to 2021, we predicted that charcoal morphs in H. viridiflavus would optimize heat absorption in cold environments, while offering protection from excessive UV radiation in N. helvetica within warm habitats; whereas picturata and abundistic morphs would thrive in humid environments, which naturally have a denser vegetation and wetter substrates producing darker ambient light, thus providing concealment advantages. While picturata and abundistic morphs did not align with our initial humidity expectations, the charcoal morph in N. helvetica is associated with UV environments, suggesting protection mechanisms against damaging solar radiation. H. viridiflavus is associated with high precipitations, which might offer antimicrobial protection. Overall, our results provide insights into the correlations between melanin-based color morphs and climate variables in snake populations. While suggestive of potential adaptive responses, future research should delve deeper into the underlying mechanisms regulating this relationship.

Impact of the COVID-19 pandemic on antidepressant consumption in the Central region of Portugal: interrupted time series.

PURPOSE: To evaluate the impact of the pandemic on the consumption of antidepressive agents in Central Portugal. METHODS: To estimate the causal effect of the pandemic an interrupted time series analysis was conducted. Data of antidepressant drugs monthly dispensed in community pharmacies between Jan-2010 and Dec-2021 were provided by the regional Health Administration. Anti-Parkinson dopaminergic agents and statins, theoretically not influenced by COVID-19 pandemics, were used as comparator series. The number of packages was converted into defined daily doses and presented as defined daily doses/1000 inhabitants/day. A Bayesian structural time-series model with CausalImpact on R/RStudio was used to predict the counterfactual. Analyses with different geographical granularity (9 sub-regions and 78 municipalities) were performed. RESULTS: When compared to counterfactual, regional consumption non-significantly increased after the pandemic declaration, with a relative effect of + 1.30% [95%CI -1.6%:4.2%]. When increasing the granularity, differences appeared between sub-region with significant increases in Baixo Mondego + 6.5% [1.4%:11.0%], Guarda + 4.4% [1.1%:7.7%] or Cova da Beira + 4.1% [0.17%:8.3%], but non-significant variation in the remaining 6 sub-regions. Differences are more obvious at municipality level, ranging from increases of + 37.00% [32.00%:42.00%] to decreases of -11.00% [-17.00%:-4.20%]. Relative impact positively correlated with percentage of elderly in the municipality (r = 0.301; p = 0.007), and negatively with population density (r=-0.243; p = 0.032). No other predicting variables were found. CONCLUSION: Antidepressant consumption suffered very slight variations at regional level after the COVID-19 pandemic declaration. Analysis with higher granularity allowed identifying municipalities with higher impact (increase or decrease). The absence of clear association patterns suggests other causal hypotheses of the differences.

Pathologic features of head and neck adult rhabdomyosarcoma: Case series.

INTRODUCTION: Adult Head and neck Rhabdomyosarcomas (HNRMS) are exceedingly rare and remain challenging for pathologists. CASES PRESENTATION: Five cases of adult HNRMS (≥19 years) were retrieved from the archives of the Department of Pathology of Hospital of Specialities in Rabat (HSR) in Morocco, over 5 years. Clinical and pathologic findings from hematoxylin and eosin slides and immunohistochemistry for Desmin and Myogenin were reviewed. CLINICAL DISCUSSION: The median age was 33, with a men's predominance (3 M/2F). Histological analysis revealed three cases of Alveolar Rhabdomyosarcoma (RMS), one Pleomorphic RMS, and one spindle cell/sclerosing RMS. In addition to the typical histology observed in each RMS, we found tricky growth patterns that could be a source of misdiagnosis. All five cases demonstrated variable positivity for Desmin and Myogenin. CONCLUSION: HNRMS cases have different pathological features than pediatric RMS cases. We identified rare subtypes such as pleomorphic and spindle cell/sclerotic RMS, which exhibit unusual morphological patterns.

Primary urethral carcinoma in female: An extremely rare case series at a single tertiary referral hospital and literature review.

INTRODUCTION AND IMPORTANCE: Primary urethral carcinoma (PUC) is exceedingly uncommon and accounts for 0.02 % of all female cancers and <1 % of female genitourinary tract malignancy. PUC in female usually presents late with a higher disease stage and, hence, has higher cancer-specific mortality. Due to its rarity, the current recommendation for the management of PUC is still unclear. CASE PRESENTATION: This study reports two rare cases of 59-year-old and 65-year-old women with PUC, presenting with chief complaint of hematuria. Urethrocystoscopy and biopsy were performed. Pathology results revealed mucinous adenocarcinoma (AC) and urothelial carcinoma (UC) of the urethra, respectively. Radiological imaging was conducted for staging. Both were diagnosed with cT4N2M0. The first patient underwent anterior pelvic exenteration with bilateral ureterocutaneoustomy (UCS), while the second patient received cisplatin-based chemotherapy before the surgery. Radiological follow-up was planned 3 months after the surgery. CLINICAL DISCUSSION: Both mucinous AC and UC are considered incredibly unusual subtypes, with no defined treatment guidelines. Anterior exenteration with or without neoadjuvant chemotherapy may be opted for advanced PUC affecting the proximal urethra and adjacent organs. Following the previous studies, in this case series, both patients (locally advanced) underwent anterior exenteration. Additionally, the UC subtype received multimodal treatment with neoadjuvant chemotherapy which was shown to improve overall survival. CONCLUSION: In conclusion, PUC is extremely rare, and the choice of management remained various. Long-term follow-up for these patients is mandatory to improve understanding of this incredibly uncommon disease.

Dataset of air pollutants (PM2.5, PM10, CO) concentrations in the export processing area of Dhaka, Bangladesh.

This study presents a valuable dataset on air quality in the densely populated Dhaka Export Processing Zone (DEPZ) of Bangladesh. It included a dataset of Particulate Matter (PM2.5, PM10) and CO concentrations with Air Quality Index (AQI) values. PM data was collected 24h, and CO data was collected 8h monthly from 2019 to 2023 using respirable dust sampler APS-113NL for PM2.5, APS-113BL for PM10, and LUTRON AQ9901SD Air Quality Monitor Data Logger used to measure CO concentration data. Data sampling locations are selected based on population density, and employment data for DEPZ is also included, highlighting a potential rise in population density. This article also forecasted pollutant concentrations, AQI values, and health hazards associated with air pollutants using the Auto Regressive Moving Average (ARIMA) model. The performance of the ARIMA model was also measured using root mean squared error (RMSE) and mean absolute error (MAE). However, this can be used to raise awareness among the public about the health hazards associated with air pollution and encourage them to take measures to reduce their exposure to air pollutants. In addition, this data can be instrumental for researchers and policymakers to assess air pollution risks, develop control strategies, and improve air quality in the DEPZ.

Changes in cannabis involvement in emergency department visits for anxiety disorders after cannabis legalization: a repeated cross-sectional study.

Background: An increasing number of countries have or are considering legalizing cannabis. One concern is that legalization of cannabis will result in increased cannabis use and in turn a higher prevalence of anxiety disorders. We examined changes in emergency department (ED) visits for anxiety disorders with cannabis involvement in Ontario, over a period that involved medical and non-medical cannabis legalization. Methods: This repeated cross-sectional population-based study identified all ED visits for anxiety disorders from residents of Ontario, Canada aged 10-105 between 2008 and 2022 (n = 15.7 million individuals). We used interrupted time series analyses to examine immediate and gradual changes in cannabis-involvement and alcohol-involvement (control condition) over four policy periods: medical cannabis legalization (January 2008-November 2015), expanded medical access (December 2015-September 2018), non-medical cannabis legalization with restrictions (October 2018-February 2020), and commercialization which overlapped with the COVID-19 pandemic (March 2020-December 2022). Poisson models were used to generate incidence rate ratios with 95% confidence intervals. Findings: Over the 14-year study, there were 438,700 individuals with one or more ED visits for anxiety disorders of which 3880 (0.89%) individuals had cannabis involvement and 6329 (1.45%) individuals had alcohol involvement. During the commercialization/COVID-19 period monthly rates of anxiety disorders with cannabis-involvement were 156% higher (0.11 vs 0.29 per 100,000 individuals) relative to the pre-legalization period, compared to a 27% increase for alcohol-involvement (0.27 vs 0.35 per 1100,000 individuals). Rates of anxiety ED visits with cannabis involvement per 100,000 individuals increased gradually over the study period with no immediate or gradual changes after expanded medical access, legalization with restrictions or commercialization/COVID-19. However, during the commercialization/COVID-19 period there were large declines in total anxiety disorder ED visits and anxiety disorder ED visits with alcohol-involvement. Consequently, during this period there was an immediate 31.4% relative increase in the proportion of anxiety visits with cannabis-involvement (incidence rate ratio [IRR], 1.31; 95% CI 1.05-1.65). Interpretation: We found large relative increases in anxiety disorder ED visits with cannabis involvement over a 14-year period involving medical and non-medical cannabis legalization. These findings may reflect increasing anxiety disorder problems from cannabis use, increasing self-medication of anxiety disorders with cannabis use, or both. The proportion of anxiety ED visits with cannabis involvement increased during the final period of the study but could have been the results of the market commercialization, COVID-19 or both and ongoing monitoring is indicated. Funding: Canadian Institutes of Health Research (grant #452360).

Impacts of Major Changes in China's COVID-19 Prevention and Control Policies on Emergency Department Visits: A Quasi-Experiment.

Objective: This study aims to evaluate the impact of COVID-19 prevention and control policies on the frequency of emergency department (ED) visits in a large tertiary hospital in central China, from January 2018 to September 2023. Methods: We conducted a multi-stage interrupted time series analysis to investigate the impact of various epidemic control policies on weekly ED visits at a tertiary hospital in Hunan Province, China. The study period ranged from January 1, 2018, to September 30, 2023, and was divided into four distinct periods: pre-epidemic, pandemic, normalized control, and end of control. Using a quasi-Poisson regression model, we examined the specific effects of these policies on emergency visits, with a particular focus on stratifying patients based on respiratory versus non-respiratory diseases. Results: Compared to the pre-pandemic period, the number of ED visits in a tertiary hospital decreased by 38.5% (95% CI: 25.1% to 49.8%) during the COVID-19 pandemic, of which the number of ED visits for respiratory diseases increased by 79.4% (95% CI: 13.2% to 177.2%) and the number of ED visits for non-respiratory diseases decreased by 45.9% (95% CI: -55.7% to -34.2%). After the end of the epidemic control, the total number of ED visits increased by 31.5% (95% CI: 19.1% to 45.0%), with the number of ED visits for respiratory diseases rising by 379.2% (95% CI: 275.9% to 511.8%), but with no significant change in the number of ED visits for non-respiratory emergencies. Conclusion: Control policies were associated with people avoiding emergency care for non-respiratory related reasons during the pandemic, while the end of control policies was associated with a sharp rise in emergency care for respiratory diseases. This study provides a scientific basis for the different changes in ED visits under the implementation of varying epidemic prevention and control policies.

Effects of pay-for-performance based antimicrobial stewardship on antimicrobial consumption and expenditure: An interrupted time series analysis.

Objectives: To evaluate the impact of pay-for-performance on antimicrobial consumption and antimicrobial expenditure in a large teaching hospital in Guangzhou, China. Methods: We collected data from hospital information system from January 2018 through September 2022 in the inpatient wards. Antimicrobial consumption was evaluated using antibiotic use density (AUD) and antibiotic use rate (AUR). The economic impact of intervention was assessed by antimicrobial expenditure percentage. The data was analyzed using interrupted time series (ITS) analysis. Results: Following the implementation of the intervention, immediate decreases in the level of AUD were observed in Department of Hematology Unit 3 (ß = -66.93 DDDs/100PD, P = 0.002), Urology (ß = -32.80 DDDs/100PD, P < 0.001), Gastrointestinal Surgery Unit 3 (ß = -11.44 DDDs/100PD, P = 0.03), Cardiac Surgery (ß = -14.30 DDDs/100PD, P = 0.01), ICU, Unit 2 (ß = -81.91 DDDs/100PD, P = 0.02) and Cardiothoracic Surgery ICU (ß = -41.52 DDDs/100PD, P = 0.05). Long-term downward trends in AUD were also identified in Organ Transplant Unit (ß = -1.64 DDDs/100PD, P = 0.02). However, only Urology (ß = -6.56 DDDs/100PD, P = 0.02) and Gastrointestinal Surgery Unit 3 (ß = -8.50 %, P = 0.01) showed an immediate decrease in AUR, and long-term downward trends in AUR were observed in Pediatric ICU (ß = -1.88 %, P = 0.05) and ICU Unit 1 (ß = -0.55 %, P = 0.02). Conclusion: This study demonstrates that the adoption of pay-for-performance effectively reduces antibiotic consumption in specific departments of a hospital in Guangzhou in the short term. However, it is important to recognize that the long-term impact of such interventions is often limited. Additionally, it should be noted that the overall effectiveness of the intervention across the entire hospital was not significant.

scMaSigPro: Differential Expression Analysis along Single-Cell Trajectories.

MOTIVATION: Understanding the dynamics of gene expression across different cellular states is crucial for discerning the mechanisms underneath cellular differentiation. Genes that exhibit variation in mean expression as a function of Pseudotime and between branching trajectories are expected to govern cell fate decisions. We introduce scMaSigPro, a method for the identification of differential gene expression patterns along Pseudotime and branching paths simultaneously. RESULTS: We assessed the performance of scMaSigPro using synthetic and public datasets. Our evaluation shows that scMaSigPro outperforms existing methods in controlling the False Positive Rate and is computationally efficient. AVAILABILITY AND IMPLEMENTATION: scMaSigPro is available as a free R package (version 4.0 or higher) under the GPL(≥2) license on GitHub at 'github.com/BioBam/scMaSigPro' and archived with version 0.03 on Zenodo at 'zenodo.org/records/12568922'.