Mechanisms Of Spread Research Articles

Acute cholangitis: Does malignant biliary obstruction vs choledocholithiasis etiology change the clinical presentation and outcomes?

Whether clinical outcomes of acute cholangitis (AC) vary by etiology is unclear. To compare outcomes in AC caused by malignant biliary obstruction (MBO) and common bile duct stones (CBDS). This retrospective study included 516 patients undergoing endoscopic retrograde cholangiopancreatography (ERCP) due to AC caused by MBO (MBO group, n = 56) and CBDS (CBDS group, n = 460). Clinical and laboratory parameters were compared between the groups. Propensity score matching (PSM) created 55 matched pairs. Confounders used in the PSM analysis were age, sex, time to ERCP, and technical success of ERCP. The primary outcome comparison was 30-d mortality. The secondary outcome comparisons were intensive care unit (ICU) admission rate, length of hospital stay (LOHS), and 30-d readmission rate. Compared with the CBDS group, the MBO group had significantly lower body temperature, percentage of abnormal white blood cell counts, and serum levels of aspartate aminotransferase, alanine aminotransferase, and creatinine. Body temperature, percent abnormal white blood cell count, and serum aspartate aminotransferase levels remained significantly lower in the MBO group in the PSM analysis. Platelet count, prothrombin time/international normalized ratio, and serum levels of alkaline phosphatase and total bilirubin were significantly higher in the MBO group. The MBO group had a significantly higher percentage of severe AC (33.9% vs 22.0%, P = 0.045) and received ERCP later (median, 92.5 h vs 47.4 h, P < 0.001). However, the two differences were not found in the PSM analysis. The 30-d mortality (5.4% vs 0.7%, P = 0.019), ICU admission rates (12.5% vs 4.8%, P = 0.028), 30-d readmission rates (23.2% vs 8.0%, P < 0.001), and LOHS (median, 16.5 d vs 7.0 d, P < 0.001) were significantly higher or longer in the MBO group. However, only LOHS remained significant in the PSM analysis. Multivariate analysis revealed that time to ERCP and multiple organ dysfunction were independent factors associated with 30-d mortality. MBO patients underwent ERCP later and thus had a worse prognosis than CBDS patients. Therefore, clinicians should remain vigilant in MBO patients with clinically suspected AC, and perform ERCP for biliary drainage as soon as possible.

Usefulness of analyzing endoscopic features in identifying the colorectal serrated sessile lesions with and without dysplasia.

Sessile serrated lesions (SSLs) are often missed on colonoscopy, and studies have shown this to be an essential cause of interstitial colorectal cancer. The SSLs with dysplasia (SSL-D+), in particular, have a faster rate of carcinogenesis than conventional tubular adenomas. Therefore, there is a clinical need for some endoscopic features with independent diagnostic value for SSL-D+s to assist endoscopists in making immediate diagnoses, thus improving the quality of endoscopic examination and treatment. To compare the characteristics of SSLs, including those with and without dysplasia (SSL-D+ and SSL-D-), based on white light and image-enhanced endoscopy, to achieve an immediate differential diagnosis for endoscopists. From January 2017 to February 2023, cases of colorectal SSLs confirmed by colonoscopy and histopathology at the Gastrointestinal Endoscopy Center of Beijing Tsinghua Changgung Hospital were collected. The general, endoscopic, and histopathological data were reviewed and analyzed to determine the diagnostic utility. Univariate analysis was used to find potential diagnostic factors, and then multivariate regression analysis was performed to derive endoscopic features with independent diagnostic values for the SSL-D+. A total of 228 patients with 253 lesions were collected as a result. There were 225 cases of colorectal SSL-D-s and 28 cases of SSL-D+s. Compared to the colorectal SSL-D-, the SSL-D+ was more common in the right colon (P = 0.027) with complex patterns of depression, nodule, and elevation based on cloud-like surfaces (P = 0.003), reddish (P < 0.001), microvascular varicose (P < 0.001), and mixed type (Pit II, II-O, IIIL, IV) of crypt opening based on Pit II-O (P < 0.001). Multifactorial logistic regression analysis indicated that lesions had a reddish color [odds ratio (OR) = 18.705, 95% confidence interval (CI): 3.684-94.974], microvascular varicose (OR = 6.768, 95%CI: 1.717-26.677), and mixed pattern of crypt opening (OR = 20.704, 95%CI: 2.955-145.086) as the independent predictors for SSL-D+s. The endoscopic feature that has independent diagnostic value for SSL-D+ is a reddish color, microvascular varicose, and mixed pattern of crypt openings.

Unusual presentation of penile giant condyloma acuminatum with spontaneous prepuce perforation: A case report

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Roles of biochemistry data, lifestyle, and inflammation in identifying abnormal renal function in old Chinese

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Applications of time series analysis in epidemiology: Literature review and our experience during COVID-19 pandemic

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

"In situ bone flap" combined with vascular pedicled mucous flap to reconstruction of skull base defect.

At present, neuroendoscopy technology has made rapid development, and great progress has been made in the operation of lesions in the saddle area of the skull base. However, the complications of cerebrospinal fluid and intracranial infection after the operation are still important and life-threatening complications, which may lead to poor prognosis. To investigate the method of in situ bone flap combined with nasal septum mucosal flap for reconstruction of enlarged skull base defect by endonasal sphenoidal approach and to discuss its application effect. Clinical data of 24 patients undergoing transnasal sphenoidal endoscopic approach in the Department of Neurosurgery, Affiliated 2 Hospital of Nantong University from January 2019 to December 2022 were retrospectively analyzed. All patients underwent multi-layer reconstruction of skull base using in situ bone flap combined with nasal septum mucosa flap. The incidence of intraoperative and postoperative cerebrospinal fluid leakage and intracranial infection were analyzed, and the application effect and technical key points of in situ bone flap combined with nasal septum mucosa flap for skull base bone reconstruction were analyzed. There were 5 cases of high flow cerebrospinal fluid (CSF) leakage and 7 cases of low flow CSF leakage. Postoperative cerebrospinal fluid leakage occurred in 2 patients (8.3%) and intracranial infection in 2 patients (8.3%), which were cured after strict bed rest, continuous drainage of lumbar cistern combined with antibiotic treatment, and no secondary surgical repair was required. The patients were followed up for 8 to 36 months after the operation, and no delayed cerebrospinal fluid leakage or intracranial infection occurred during the follow-up. Computed tomography reconstruction of skull base showed satisfactory reconstruction after surgery. The use of in situ bone flap combined with vascular pedicled mucous flap to reconstruction of skull base defect after endonasal sphenoidal approach under neuroendoscopy has a lower incidence of cerebrospinal fluid leakage and lower complications, which has certain advantages and is worthy of clinical promotion.

Meta-analysis of the efficacy and safety of daratumumab in the treatment of multiple myeloma.

The treatment of multiple myeloma has significantly progressed over the past half-century. The purpose of this study was to perform a systematic review and meta-analysis in order to explore the efficacy and safety of daratumumab in treating multiple myeloma. To explore the efficacy and safety of daratumumab in treating multiple myeloma. A systematic literature search was performed using Chinese and English databases, including the China National Knowledge Infrastructure, Wanfang, China Biology Medicine, VIP, the Cochrane Library, Embase, and PubMed. The search encompassed studies in treating multiple myeloma with daratumumab, spanning from the inception of the database to June 2023. Revman 5.1 software was used for analysis. Our analysis included eight English articles and one Chinese article of high quality. The meta-analysis results indicated that compared to other therapies, daratumumab could improve the overall response rate (ORR) [odds ratio (OR) = 2.67, 95% confidence interval (CI) = 2.01, 3.53, Z = 6.85, P < 0.00001], complete remission (CR) (OR = 2.87, 95%CI = 2.16, 3.83, Z = 7.23, P < 0.00001) and progression-free survival (PFS) time (hazard ratio = 0.48, 95%CI = 0.38,0.60, Z = 6.54, P < 0.00001) in patients with multiple myeloma. These differences were statistically significant. Additionally, these results suggested that daratumumab increases the risk of neutropenia and thrombocytopenia with minimal effect on the incidences of anemia and upper respiratory tract infections. Daratumumab can improve ORR, CR rate, and PFS in patients with multiple myeloma. It also increases the risk of neutropenia and thrombocytopenia, necessitating careful monitoring during its clinical application.

First platelet transfusion refractoriness in a patient with acute myelocytic leukemia: A case report

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Axenfeld-Reiger syndrome: A search for the missing links.

Axenfeld-Rieger syndrome (ARS) is a rare cause of congenital glaucoma and may result in loss of vision. ARS is mostly autosomal dominant in nature characterized by developmental abnormalities in the angle of anterior chamber and iris of the eye, also associated with structural abnormalities in the body. To study and observe the demographics and clinical findings in a very rare ocular disease known as ARS. Case records of symptomatic patients attending Ophthalmology outpatient department and diagnosed to have ocular hypertension or glaucoma in 3 years from March 2017 to March 2020 were evaluated to search for cases diagnosed with ARS. Records of all patients diagnosed with ARS were then analysed for demographic and clinical characterization as well as management and success of therapy. Eight out of ten patients with positive clinical signs were symptomatic and had glaucoma. One of these patients had limbal stem cell deficiency and another had vernal keratoconjunctivitis. Clinical characterization of ARS is important for making a definitive diagnosis and determining prognosis.

Clinical efficacy and safety of Guipi decoction combined with escitalopram oxalate tablets in patients with depression

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Simultaneous thyroglossal duct cyst with parathyroid cyst: A case report

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Self-management of osteoarthritis while waiting for total knee arthroplasty during the COVID-19 pandemic among older Malaysians

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Artificial intelligence technology and ultrasound-guided nerve block for analgesia in total knee arthroplasty

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Surgical treatment of severe anterior capsular organized hard core cataract: A case report

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Reference values of gait parameters in healthy Chinese university students: A cross-sectional observational study

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Artificial intelligence and machine learning in motor recovery: A rehabilitation medicine perspective

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Rituximab combined with Bruton tyrosine kinase inhibitor to treat elderly diffuse large B-cell lymphoma patients: Two case reports

Time series analysis is a valuable tool in epidemiology that complements the classical epidemiological models in two different ways: Prediction and forecast. Prediction is related to explaining past and current data based on various internal and external influences that may or may not have a causative role. Forecasting is an exploration of the possible future values based on the predictive ability of the model and hypothesized future values of the external and/or internal influences. The time series analysis approach has the advantage of being easier to use (in the cases of more straightforward and linear models such as Auto-Regressive Integrated Moving Average). Still, it is limited in forecasting time, unlike the classical models such as Susceptible-Exposed-Infectious-Removed. Its applicability in forecasting comes from its better accuracy for short-term prediction. In its basic form, it does not assume much theoretical knowledge of the mechanisms of spreading and mutating pathogens or the reaction of people and regulatory structures (governments, companies, etc. ). Instead, it estimates from the data directly. Its predictive ability allows testing hypotheses for different factors that positively or negatively contribute to the pandemic spread; be it school closures, emerging variants, etc. It can be used in mortality or hospital risk estimation from new cases, seroprevalence studies, assessing properties of emerging variants, and estimating excess mortality and its relationship with a pandemic.

Research on panic spread and decision behaviour in a delayed SEIR evolutionary game model under an emergency

Taking major emergencies as the research background, the transmission mechanism for panic spread and the decision behaviour of the opinion field are explored in this paper. By using evolutionary game theory to explore the game relationship between the official opinion field and the public opinion field and by considering the existence of strategy dependency in the same game group, the dependence coefficient is introduced to improve replicator dynamics. The dynamic delayed SEIR evolutionary game model is built by combining the epidemic model with the delay effect within the group, and the stability of the proposed model is analysed. The research results show that the strategy dependency among the same game group has positive and negative effects on the evolution process. The evolution of the dynamic delayed panic SEIR evolutionary game spread model under the effect of a positive effect is simulated. The results suggest that the official opinion field and the public opinion field should actively deal with emergencies, formulate effective control strategies to make the panic within the group disappear, and provide theoretical guidance for the relevant government to formulate plans.

Spatio-temporal distribution and dynamics of antibiotic resistance genes in a water-diversion lake, China

The distribution and dynamics of antibiotic resistance genes (ARGs) in water-diversion lakes are poorly understood. In this study, two comparative in situ investigations of ARG profiles targeting water diversion (DP) and non-diversion periods (NDP) were conducted in Luoma Lake, a vital transfer node for the eastern route of the South-to-North Water Diversion Project in China. The results demonstrated significant spatiotemporal variations in ARG contamination and notable differences in the co-occurrence patterns of ARGs and bacterial communities between DP and NDP. Correlations among ARGs with the 16 S rRNA, and mobile genetic elements indicate that horizontal gene transfer (HGT) and vertical gene transfer (VGT) in NDP, but only HGT in DP, were the primary mechanisms of ARG proliferation and spread, implying that water diversion could be an essential control of the transfer pattern of ARGs in a lake environment. The null model analysis indicated that stochastic processes, with predominant driver of ecological drift in the lake mainly drove the assembly of ARGs. Partial least squares structural equation modeling was developed to analyze the causal effects of the factors in shaping ARG dynamics and identify the major driving forces in the DP and NDP.

Unveiling Rare Pathogens and Antibiotic Resistance in Tanzanian Cholera Outbreak Waters.

The emergence of antibiotic resistance is a global health concern. Therefore, understanding the mechanisms of its spread is crucial for implementing evidence-based strategies to tackle resistance in the context of the One Health approach. In developing countries where sanitation systems and access to clean and safe water are still major challenges, contamination may introduce bacteria and bacteriophages harboring antibiotic resistance genes (ARGs) into the environment. This contamination can increase the risk of exposure and community transmission of ARGs and infectious pathogens. However, there is a paucity of information on the mechanisms of bacteriophage-mediated spread of ARGs and patterns through the environment. Here, we deploy Droplet Digital PCR (ddPCR) and metagenomics approaches to analyze the abundance of ARGs and bacterial pathogens disseminated through clean and wastewater systems. We detected a relatively less-studied and rare human zoonotic pathogen, Vibrio metschnikovii, known to spread through fecal--oral contamination, similarly to V. cholerae. Several antibiotic resistance genes were identified in both bacterial and bacteriophage fractions from water sources. Using metagenomics, we detected several resistance genes related to tetracyclines and beta-lactams in all the samples. Environmental samples from outlet wastewater had a high diversity of ARGs and contained high levels of blaOXA-48. Other identified resistance profiles included tetA, tetM, and blaCTX-M9. Specifically, we demonstrated that blaCTX-M1 is enriched in the bacteriophage fraction from wastewater. In general, however, the bacterial community has a significantly higher abundance of resistance genes compared to the bacteriophage population. In conclusion, the study highlights the need to implement environmental monitoring of clean and wastewater to inform the risk of infectious disease outbreaks and the spread of antibiotic resistance in the context of One Health.