Disease Severity Research Articles

Diet-induced obesity affects influenza disease severity and transmission dynamics in ferrets.

Obesity, and the associated metabolic syndrome, is a risk factor for increased disease severity with a variety of infectious agents, including influenza virus. Yet, the mechanisms are only partially understood. As the number of people, particularly children, living with obesity continues to rise, it is critical to understand the role of host status on disease pathogenesis. In these studies, we use a diet-induced obese ferret model and tools to demonstrate that, like humans, obesity resulted in notable changes to the lung microenvironment, leading to increased clinical disease and viral spread to the lower respiratory tract. The decreased antiviral responses also resulted in obese animals shedding higher infectious virus for a longer period, making them more likely to transmit to contacts. These data suggest that the obese ferret model may be crucial to understanding obesity's impact on influenza disease severity and community transmission and a key tool for therapeutic and intervention development for this high-risk population.

Abnormal EEG microstates in Alzheimer's disease: predictors of β-amyloid deposition degree and disease classification.

Electroencephalography (EEG) microstates are used to study cognitive processes and brain disease-related changes. However, dysfunctional patterns of microstate dynamics in Alzheimer's disease (AD) remain uncertain. To investigate microstate changes in AD using EEG and assess their association with cognitive function and pathological changes in cerebrospinal fluid (CSF). We enrolled 56 patients with AD and 38 age- and sex-matched healthy controls (HC). All participants underwent various neuropsychological assessments and resting-state EEG recordings. Patients with AD also underwent CSF examinations to assess biomarkers related to the disease. Stepwise regression was used to analyze the relationship between changes in microstate patterns and CSF biomarkers. Receiver operating characteristics analysis was used to assess the potential of these microstate patterns as diagnostic predictors for AD. Compared with HC, patients with AD exhibited longer durations of microstates C and D, along with a decreased occurrence of microstate B. These microstate pattern changes were associated with Stroop Color Word Test and Activities of Daily Living scale scores (all P < 0.05). Mean duration, occurrences of microstate B, and mean occurrence were correlated with CSF Aβ 1-42 levels, while duration of microstate C was correlated with CSF Aβ 1-40 levels in AD (all P < 0.05). EEG microstates are used to predict AD classification with moderate accuracy. Changes in EEG microstate patterns in patients with AD correlate with cognition and disease severity, relate to Aβ deposition, and may be useful predictors for disease classification.

Advanced Integration of Machine Learning Techniques for Accurate Segmentation and Detection of Alzheimer’s Disease

Alzheimer’s disease is a common type of neurodegenerative condition characterized by progressive neural deterioration. The anatomical changes associated with individuals affected by Alzheimer’s disease include the loss of tissue in various areas of the brain. Magnetic Resonance Imaging (MRI) is commonly used as a noninvasive tool to assess the neural structure of the brain for diagnosing Alzheimer’s disease. In this study, an integrated Improved Fuzzy C-means method with improved watershed segmentation was employed to segment the brain tissue components affected by this disease. These segmented features were fed into a hybrid technique for classification. Specifically, a hybrid Convolutional Neural Network–Long Short-Term Memory classifier with 14 layers was developed in this study. The evaluation results revealed that the proposed method achieved an accuracy of 98.13% in classifying segmented brain images according to different disease severities.

Real-World Long-Term Effectiveness of Risankizumab Among Patients with Moderate-to-Severe Psoriasis: Analysis from an International Medical Chart Review (RAPID) Study.

Real-world data on the efficacy of risankizumab (RZB) in clinical moderate-to-severe plaque psoriasis (PsO) are limited. The RAPID study assessed real-world clinical and patient-reported outcomes in RZB-treated PsO patients using data collected from dermatologists in Canada, the Czech Republic, Germany, Japan, and Poland. This ongoing, retrospective chart review collected data from medical records of RZB-treated adults with moderate-to-severe PsO (09/2022-06/2023). Eligible patients received RZB, had ≥ 12months of medical records after RZB initiation (index date), and had Psoriasis Area and Severity Index (PASI), Investigator Global Assessment (IGA), or static Physician's Global Assessment (sPGA) scores ≥ 3months before and up to 18months after the index date. The proportion of patients achieving a clear/almost clear PsO (IGA/sPGA = 0/1), PASI ≤ 1, Dermatology Life Quality Index (DLQI) = 0/1, and a 90%/100% improvement from baseline in PASI as well as the mean changes in PASI, DLQI, itch, and skin pain scores at 12 and 18 months were reported for patients with non-missing assessments at baseline and 12months. Most patients (66.4%) were male, 74.0% were biologic naïve, and 73.0% had scalp PsO. Mean baseline IGA/sPGA was 3.7 ± 0.5, with a mean PASI of 23.3 ± 11.8. After 12months, 86.1% of patients reported IGA/sPGA ≤ 1, and 75.7% achieved PASI90; these further increased to 91.1% and 80.5% at 18months. DLQI, itch, and skin pain scores improved over time. These data demonstrated the durable, real-world effectiveness of RZB in patients with moderate-to-severe PsO through continued improvement in disease and symptom severity over 18months, with most of the patients reporting clear/almost clear skin.

Evaluation of erythrocyte and reticulocyte indices in a newborn with severe hemolytic disease

Currently, significant progress has been made in the prevention, diagnosis and treatment of hemolytic disease of the fetus and newborn. However, the development of anemia in a newborn child due to this disease remains an urgent problem for both neonatologists and pediatricians. In such children, a comprehensive assessment of the hemogram is of particular importance. The study of erythrocyte and reticulocyte parameters of a general blood test is necessary to determine prognostic criteria for the restoration of hematopoiesis and determine the status of iron deficiency. A clinical observation of a newborn child with the development of anemia due to hemolytic disease of the fetus and newborn is presented (clinical case). A dynamic analysis of reticulocyte parameters (absolute and relative numbers) and reticulocyte fractions was carried out. An increase in reticulocytes (absolute and relative numbers) was established, mainly due to the fraction of immature reticulocytes during the development of anemia in a newborn child. Such indicators as the hemoglobin content in reticulocytes, the average hemoglobin content in an erythrocyte, the average hemoglobin concentration in an erythrocyte in a general blood test in an infant during the neonatal period remained within the reference values. The data obtained made it possible to determine a personalized approach to the treatment of anemia and avoid blood transfusion in this child.

Development and Validation of an Explainable Deep Learning Model to Predict In-Hospital Mortality for Patients With Acute Myocardial Infarction: Algorithm Development and Validation Study.

Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases and is associated with a high risk of in-hospital mortality. However, the current deep learning models for in-hospital mortality prediction lack interpretability. This study aims to establish an explainable deep learning model to provide individualized in-hospital mortality prediction and risk factor assessment for patients with AMI. In this retrospective multicenter study, we used data for consecutive patients hospitalized with AMI from the Chongqing University Central Hospital between July 2016 and December 2022 and the Electronic Intensive Care Unit Collaborative Research Database. These patients were randomly divided into training (7668/10,955, 70%) and internal test (3287/10,955, 30%) data sets. In addition, data of patients with AMI from the Medical Information Mart for Intensive Care database were used for external validation. Deep learning models were used to predict in-hospital mortality in patients with AMI, and they were compared with linear and tree-based models. The Shapley Additive Explanations method was used to explain the model with the highest area under the receiver operating characteristic curve in both the internal test and external validation data sets to quantify and visualize the features that drive predictions. A total of 10,955 patients with AMI who were admitted to Chongqing University Central Hospital or included in the Electronic Intensive Care Unit Collaborative Research Database were randomly divided into a training data set of 7668 (70%) patients and an internal test data set of 3287 (30%) patients. A total of 9355 patients from the Medical Information Mart for Intensive Care database were included for independent external validation. In-hospital mortality occurred in 8.74% (670/7668), 8.73% (287/3287), and 9.12% (853/9355) of the patients in the training, internal test, and external validation cohorts, respectively. The Self-Attention and Intersample Attention Transformer model performed best in both the internal test data set and the external validation data set among the 9 prediction models, with the highest area under the receiver operating characteristic curve of 0.86 (95% CI 0.84-0.88) and 0.85 (95% CI 0.84-0.87), respectively. Older age, high heart rate, and low body temperature were the 3 most important predictors of increased mortality, according to the explanations of the Self-Attention and Intersample Attention Transformer model. The explainable deep learning model that we developed could provide estimates of mortality and visual contribution of the features to the prediction for a patient with AMI. The explanations suggested that older age, unstable vital signs, and metabolic disorders may increase the risk of mortality in patients with AMI.

Clinical and Demographic Characteristics of Pyruvate Kinase Deficiency Patients: A Comprehensive Case Series Analysis.

Introduction Pyruvate kinase deficiency (PKD) is a rare autosomal recessive disorder characterized by mutations in the PKLR gene, causing impaired glycolysis in red blood cells and leading to diverse clinical manifestations. The prevalence of PKD in Saudi Arabia remains understudied, particularly in the context of consanguinity and non-specialized medical facilities. Methods We conducted a retrospective analysis of seven PKD patients of Arab ethnicity, focusing on demographics, medical history, clinical features, laboratory results, treatments, and outcomes. Results Our patient cohort comprised five males and two females, aged 10 to 38 years, of Arab ethnicity. Consanguinity was prevalent, and hereditary connections were identified in five patients. PKD exhibited varying clinical presentations, with early-onset symptoms including neonatal jaundice and symptomatic anemia. One patient experienced severe hepatic disease progression leading to multiorgan failure. Blood transfusions were universally required, indicating the severity of the disorder. Anemia severity varied among patients, with diverse hematological irregularities. Splenectomy was performed for most patients, improving hemoglobin levels and transfusion needs in some cases. Iron chelation was administered, although iron overload persisted. Thrombocytosis and venous thromboembolism were observed post splenectomy. Jaundice and gallstones were common, leading to cholecystectomy. Laboratory findings remained consistent, with heightened reticulocyte counts and altered enzyme levels. Discussion PKD is a rare disorder characterized by diverse clinical manifestations. Prevalence estimation is complex due to various factors, and its diagnosis is challenged by clinical similarities with other disorders. Our cohort exhibited a spectrum of complications, highlighting the necessity for tailored interventions. Iron overload remained a concern, necessitating continuous monitoring. Although endocrine disorders and osteoporosis were absent in our cohort, vigilance is essential due to the disease's progressive nature. Genetic factors were prominent, supporting the genetic basis of PKD. Splenectomy improved anemia but had a limited impact on gallstones. Iron overload management and bone health remain crucial considerations. Conclusion This study offers comprehensive insights into the clinical and demographic characteristics of PKD patients, illustrating the complex nature of the disorder. The findings underscore the need for personalized management strategies and vigilant monitoring to address the diverse clinical manifestations and challenges associated with PKD.

Accumulation of Lipid Droplets Induced by Listeria monocytogenes in Macrophages: Implications for Survival and Evasion of Innate Immunity.

Listeriosis, caused by Listeria monocytogenes (L.m.), poses a significant public health concern as one of the most severe foodborne diseases. The pathogenesis of L.m. involves critical steps such as phagosome rupture and escape upon internalization. Throughout infection, L.m. influences various host processes, including lipid metabolism pathways, yet the role of lipid droplets (LDs) remains unclear. Here, we reported a rapid, time-dependent increase in LD formation in macrophages induced by L.m. LD biogenesis was found to be dependent on L.m. viability and virulence genes, particularly on the activity of the pore-forming protein listeriolysin O (LLO). The prevention of LD formation by inhibiting diacylglycerol O-acyltransferase 1 (DGAT1) and cytosolic phospholipase A2 (cPLA2) significantly reduced intracellular bacterial survival, impaired prostaglandin E2 (PGE2) synthesis, and decreased IL-10 production. Additionally, inhibiting LD formation led to increased levels of TNF-α and IFN-β. Collectively, our data suggest a role for LDs in promoting L.m. cell survival and evasion within macrophages.

Selenium Nanomaterials Enhance Sheath Blight Resistance and Nutritional Quality of Rice: Mechanisms of Action and Human Health Benefit.

In the current work, the foliar application of selenium nanomaterials (Se0 NMs) suppressed sheath blight in rice (Oryza sativa). The beneficial effects were nanoscale specific and concentration dependent. Specifically, foliar amendment of 5 mg/L Se0 NMs decreased the disease severity by 68.8% in Rhizoctonia solani-infected rice; this level of control was 1.57- and 2.20-fold greater than that of the Se ions with equivalent Se mass and a commercially available pesticide (Thifluzamide). Mechanistically, (1) the controlled release ability of Se0 NMs enabled a wider safe concentration range and greater bioavailability to Se0 NMs, and (2) transcriptomic and metabolomic analyses demonstrated that Se0 NMs simultaneously promoted the salicylic acid- and jasmonic-acid-dependent acquired disease resistance pathways, antioxidative system, and flavonoid biosynthesis. Additionally, Se0 NMs improved rice yield by 31.1%, increased the nutritional quality by 6.4-7.2%, enhanced organic Se content by 44.8%, and decreased arsenic and cadmium contents by 38.7 and 42.1%, respectively, in grains as compared with infected controls. Human simulated gastrointestinal tract model results showed that the application of Se0 NMs enhanced the bioaccessibility of Se in grains by 22.0% and decreased the bioaccessibility of As and Cd in grains by 20.3 and 13.4%, respectively. These findings demonstrate that Se0 NMs can serve as an effective and sustainable strategy to increase food quality and security.

Acute pancreatitis associated with pleural effusion: MDCT manifestations and anatomical basis.

Acute pancreatitis (AP) is a severe condition with complications that can impact multiple organ systems throughout the body. Specifically, the diffusion of peripancreatic effusion to the pleural cavity is a significant phenomenon in AP. However, its pathways and implications for disease severity are not fully understood. This study aims to investigate the anatomical routes of peripancreatic effusion diffusion into the pleural cavity in patients with AP and to analyze the correlation between the severity of pleural effusion (PE) and the computed tomography severity index (CTSI) and acute physiology and chronic health evaluation II (APACHE II) scoring system. 119 patients with AP admitted to our institution were enrolled in this study (mean age 50 years, 74 male and 45 female). Abdominal CT was performed, and the CTSI and APACHE II index were used to evaluate the severity of the AP, Meanwhile, the prevalence and semiquantitative of PE were also mentioned. The anatomical pathways of peripancreatic effusion draining to pleural were analyzed. Finally, the correlation relationship between the severity of AP and the PE was analyzed. In 119 patients with AP, 74.8% of patients had PE on CT. The anatomic pathways of peripancreatic effusion draining to pleural included esophageal hiatus in 33.7% of patients, aortic hiatus in 6.7% of patients and inferior vena cava hiatus in 3.37% of patients. The rating of PE on CT was correlated with CTSI scores (r= 0.449, P= 0.000) and was slightly correlated with the APACHE II scores (r= 0.197, P= 0.016). PE is a common complication of AP, which can be caused by anatomic pathways such as diaphragmatic hiatus. Due to its correlation with the CTSI score, the PE may be a supplementary indicator in determining the severity of AP.

In vitro and in vivo antifungal efficacy of individual and consortium Bacillus strains in controlling potato black scurf and possible development of spore-based fungicide

Potato is a major crop whose yield and quality are impacted by the pathogenic fungus Rhizoctonia solani. Here, a control approach based on the use of endophytic bacillus spores and bacterial consortia was assessed as an innovative solution to improve tuber productivity and health. Endophytic potato bacteria extracted from several organs (leaves, stems and roots) evaluated for their antagonistic activity against R. solani by the double culture technique and their ability to rot potato slices. Additionally, the ability of Bacillus strains to generate hydrolytic enzymes and produce lipopeptides (using ESI-MS/MS mass spectrometry) was assessed. Then, the effectiveness of selected bacterial strains, either individually or in consortium (after carrying out compatibility tests) in the suppression of R. solani was evaluated in the greenhouse. Six endophytes not affecting potato tubers and having an antagonistic effect against R. solani were identified: B. halotolerans SpS5, B. velezensis KnL15, B. aryabhattai FaR1, B. amyloliquefaciens LiR9, B. haynesii ReR10 which produce several types and combinations of lipopeptides belonging to different families (iturins, fengycins and surfactins) as well as hydrolytic enzymes (chitinase, protease and cellulase). The vegetative and sporulating strain SpS5, as well as the 5 other Bacillus and their Sps5-based consortia, were found to be very effective in increasing the yield of healthy, high-quality tubers and reducing disease severity. Based on the results mentioned above, a microbial fungicide from the spores of (i) B. halotolerans SpS5 for which we sequenced the whole genome or (ii) bacterial consortia could be developed to combat black scurf potato and other fungal infections.

Application of artificial intelligence for the classification of the clinical outcome and therapy in patients with viral infections: The case of COVID-19.

With the end of the coronavirus disease 2019 (COVID-19) pandemic, it becomes intriguing to observe the impact of innovative digital technologies on the diagnosis and management of diseases, in order to improve clinical outcomes for patients. The research aims to enhance diagnostics, prediction, and personalized treatment for patients across three classes of clinical severity (mild, moderate, and severe). What sets this study apart is its innovative approach, wherein classification extends beyond mere disease presence, encompassing the classification of disease severity. This novel perspective lays the foundation for a crucial decision support system during patient triage. An artificial neural network, as a deep learning technique, enabled the development of a complex model based on the analysis of data collected during the process of diagnosing and treating 1000 patients at the Tešanj General Hospital, Bosnia and Herzegovina. The final model achieved a classification accuracy of 82.4% on the validation data set, which testifies to the successful application of the artificial neural network in the classification of clinical outcomes and therapy in patients infected with viral infections. The results obtained show that expert systems are valuable tools for decision support in healthcare in communities with limited resources and increased demands. The research has the potential to improve patient care for future epidemics and pandemics.

Long-term safety and efficacy of Glycopyrronium tosylate in patients with primary axillary hyperhidrosis: a systematic review and meta-analysis

Background: Hyperhidrosis, a condition characterized by excessive sweating, affects ≈ 4.8% of the US population. (1) Glycopyrronium tosylate (GT) is a topical anticholinergic drug approved for primary axillary hyperhidrosis (PAH) treatment. (2) However, no meta-analysis exists on GT’s long-term safety and efficacy. Aim: To evaluate the long-term safety and efficacy of GT as a treatment for PAH. Methods: We systematically searched PubMed, Scopus, and ClinicalTrials.gov. Study selection, quality assessment, and data extraction followed the eligibility criteria. The primary endpoints were changes in the Dermatology Life Quality Index (DLQI), Hyperhidrosis Disease Severity Scale (HDSS), and Axillary Sweating Daily Diary (ASDD) or Axillary Sweating Daily Diary – Change (ASDD-C). Safety endpoints included mydriasis, dry mouth, urinary hesitation, and treatment-emergent adverse events (TEAEs). Statistical analysis was conducted using RevMan 5.1. Results: Our meta-analysis demonstrated that topical GT is clinically effective in treating PAH. GT significantly improves the quality of life of patients, as measured by the DLQI (MD = -2.81, 95% CI = [-4.04, -1.59],P = 0.001), HDSS (OR = 3.72, 95% CI = [2.92, 4.74], P = 0.001), and ASDD/ASDD-C (OR = 4.91, 95%CI = [3.75, 6.44], P = 0.001). Adverse effects such as mydriasis, dry mouth, and urinary hesitation were observed; however, no difference was found between GT and control groups in serious TEAEs, deaths, and severe TEAEs. Conclusion: Topical GT is effective with minimal side effects, providing a valuable option for patients unresponsive to or experiencing significant side effects from other treatments.

Enhanced Therapeutic Effects of Human Mesenchymal stem Cells Transduced with Secreted Klotho in a Murine Experimental Autoimmune Encephalomyelitis Model.

Treatment of multiple sclerosis (MS) remains a major challenge. The aim of this study was to evaluate the therapeutic potential of mesenchymal stem cells (MSCs) engineered with secreted Klotho (SKL) in an experimental autoimmune encephalomyelitis (EAE) mouse model of MS. EAE was induced in mice. MSCs or MSCs engineered with SKL (SKL-MSCs) were administered to EAE mice at the onset of disease. Hematoxylin-eosin and luxol fast blue staining were performed to evaluate histopathological changes. Expression of pro-inflammatory (TNF-α, IFN-γ, and IL-17) and anti-inflammatory (IL-10) cytokines was determined in the spinal cord using real-time PCR. Spinal cords were then processed for immunohistochemistry of the aforementioned cytokines. The frequencies of Th1, Th17, and regulatory T (Treg) cells were evaluated by flow cytometry of the spleen. The results showed that SKL-MSCs decreased clinical scores and reduced demyelination and inflammatory infiltration in the spinal cord more significantly than MSCs. Compared to MSCs, SKL-MSCs also exhibited a more profound capability of decreasing expression of TNF-α, IFN-γ, and IL-17 and increasing expression of IL-10 in the spinal cord with an enhanced homing to the inflamed tissue. Moreover, SKL-MSCs decreased the frequencies of Th1 and Th17 cells and increased the frequency of Treg cells in the spleen more potently than MSCs. Taken together, these findings demonstrate that SKL overexpression enhances the therapeutic potential of MSCs, as evidenced by significantly improved disease severity, decreased inflammation and tissue damage in the spinal cord, and a promoted shift in the Th17/Treg balance towards the anti-inflammatory Treg side in the EAE mice.

Molecular characteristics and phylogenetic analysis of pigeon paramyxovirus type 1 isolates from pigeon meat farms in Shanghai (2009–2012)

The majority of pigeon paramyxovirus type 1 (PPMV-1) strains are generally non-pathogenic to chickens; however, they can induce severe illness and high mortality rates in pigeons, leading to substantial economic repercussions. The genomes of 11 PPMV-1 isolates from deceased pigeons on meat pigeon farms during passive monitoring from 2009 to 2012 were sequenced and analyzed using polymerase chain reaction and phylogenetic analysis. The complete genome lengths of 11 isolates were approximately 15,192 nucleotides, displaying a consistent gene order of 3′-NP-P-M-F-HN-L-5′. ALL isolates exhibited the characteristic motif of 112RRQKRF117 at the fusion protein cleavage site, which is characteristic of velogenic Newcastle disease virus. Moreover, multiple mutations have been identified within the functional domains of the F and HN proteins, encompassing the fusion peptide, heptad repeat region, transmembrane domains, and neutralizing epitopes. Phylogenetic analysis based on sequences of the F gene unveiled that all isolates clustered within genotype VI in class II. Further classification identified at least two distinct sub-genotypes, with seven isolates classified as sub-genotype VI.2.1.1.2.2, whereas the others were classified as sub-genotype VI.2.1.1.2.1. This study suggests that both sub-genotypes were implicated in severe disease manifestation among meat pigeons, with sub-genotype VI.2.1.1.2.2 displaying an increasing prevalence among Shanghai’s meat pigeon population since 2011. These results emphasize the value of developing pigeon-specific vaccines and molecular diagnostic tools for monitoring and proactively managing potential PPMV-1 outbreaks.

Evaluation of SARS-CoV-2 interferon gamma release assay in BNT162b2 vaccinated healthcare workers.

To predict protective immunity to SARS-CoV-2, cellular immunity seems to be more sensitive than humoral immunity. Through an Interferon-Gamma (IFN-γ) Release Assay (IGRA), we show that, despite a marked decrease in total antibodies, 94.3% of 123 healthcare workers have a positive cellular response 6 months after inoculation with the 2nd dose of BNT162b2 vaccine. Despite the qualitative relationship found, we did not observe a quantitative correlation between IFN-γ and IgG levels against SARS-CoV-2. Using stimulated whole blood from a subset of participants, we confirmed the specific T-cell response to SARS-CoV-2 by dosing elevated levels of the IL-6, IL-10 and TNF-α. Through a 20-month follow-up, we found that none of the infected participants had severe COVID-19 and that the first positive cases were only 12 months after the 2nd dose inoculation. Future studies are needed to understand if IGRA-SARS-CoV-2 can be a powerful diagnostic tool to predict future COVID-19 severe disease, guiding vaccination policies.

Severe fever with thrombocytopenia syndrome with central nervous system symptom onset: a case report and literature review

BackgroundSevere fever with thrombocytopenia syndrome (SFTS) is a natural focal disease transmitted mainly by tick bites, and the causative agent is SFTS virus (SFTSV). SFTS can rapidly progress to severe disease, with multiple-organ failure (MOF) manifestations such as shock, respiratory failure, disseminated intravascular coagulation (DIC) and death, but cases of SFTS patients with central nervous system (CNS) symptoms onset and marked persistent involuntary shaking of the perioral area and limbs have rarely been reported.Case presentationA 69-year-old woman with fever and persistent involuntary shaking of the perioral area and limbs was diagnosed with SFTS with CNS symptom onset after metagenomic next-generation sequencing (mNGS) of cerebrospinal fluid (CSF) and peripheral blood identified SFTSV. The patient developed a cytokine storm and MOF during the course of the disease, and after aggressive antiviral, glucocorticoid, and gamma globulin treatments, her clinical symptoms improved, her laboratory indices returned to normal, and she had a good prognosis.ConclusionThis case gives us great insight that when patients with CNS symptoms similar to those of viral encephalitis combined with thrombocytopenia and leukopenia are encountered in the clinic, it is necessary to consider the possibility of SFTS involving the CNS. Testing for SFTSV nucleic acid in CSF and blood (mNGS or polymerase chain reaction (PCR)) should be carried out, especially in critically ill patients, and treatment should be given accordingly.

Single-Center In-Hospital and Outpatient Opioid Use for Lower Extremity Arterial Disease.

The pain associated with lower extremity arterial disease is difficult to treat, even with lower extremity revascularization. We sought to evaluate in-hospital and post-operative opioid usage in patients with different disease severities and treatments for lower extremity vascular disease. A retrospective review was performed for all hospital encounters for patients with an International Classification of Diseases (ICD) code consistent with lower extremity arterial disease admitted to a single center between January 2018 and March 2023. Cases included patients admitted to the hospital with a primary diagnosis of lower extremity arterial disease. These patients were subdivided based on disease severity, treatment type, and comorbid diagnosis of diabetes mellitus. The analysis focused on in-hospital opioid use frequency and dosage among these patients. The control group (CON) included encounters for patients admitted with a secondary diagnosis of lower extremity atherosclerotic disease. A total of 438 patients represented by all the analyzed encounters were then reviewed for the number and type of vascular procedures performed as well as opioid use in the outpatient setting for one year. Critical limb ischemia (CLI) encounters were more likely to use opioids as compared to the CON and peripheral arterial disease (PAD) without rest pain, ulcer or gangrene groups (CLI 67.9% (95% CI: 63.6%-71.6%) versus CON 52.1% (95% CI: 48.5%-55.7%), p < 0.001 and CLI 67.9% (95% CI: 63.6%-71.6%) versus PAD 50.2% (95% CI: 42.6%-57.4%), p < 0.001). Opioid use was also more common in encounters for gangrene and groups treated with revascularization (REVASC) and amputation (AMP) as compared to CON (gangrene 74.5% (95% CI: 68.5%-82.1%) versus CON 52.1% (95% CI: 48.5%-55.7%), p < 0.01; REVASC 58.3% (95% CI: 57.3%-66.4%) versus CON 52.1% (95% CI: 48.5%-55.7%), p =0.01; and AMP 72.3% (95% CI: 62.1%-74.0%) versus CON 52.1% (95% CI: 48.5%-55.7%), p < 0.01). Significantly increased oral opioid doses per day (MME/day) were not noted for any of the investigated groups as compared to the CON. In the outpatient setting, 186 (42.5% (95% CI: 37.2%-46.4%)) patients were using opioids one month after the most recent vascular intervention. By one year, 31 (7.1% (95% CI: 1.30%-7.70%)) patients were still using opioids. No differences in opioid usage were noted for patients undergoing single versus multiple vascular interventions at one year. Patients undergoing certain vascular surgery procedures were more likely to be using opioids at one year. Patients with CLI and gangrene as well as those undergoing vascular treatment have a greater frequency of opioid use during hospital encounters as compared to those patients with less severe disease and undergoing conservative management, respectively. However, these findings do not equate to higher doses of opioids used during hospitalization. Patients undergoing multiple vascular procedures are not more likely to be using opioids long-term (at one year) as compared to those patients treated with single vascular procedures.

Validation of the Individualized Metabolic Surgery score in predicting long-term remission of diabetes after duodenal switch-type procedures.

To validate the Individualized Metabolic Surgery (IMS) score and assess long-term remission of type 2 diabetes (T2D) after duodenal switch (DS)-type procedures in patients with obesity. In addition, to help guide metabolic procedure selection for those patients categorized as having severe T2D. This is a retrospective single cohort study of all patients with T2D and severe obesity, who underwent DS-type procedures at a single institution from December 2010 to December 2018. Study endpoints included validating the IMS score in our cohort and evaluating the impact of DS-type procedures on long-term (≥ 5 years) remission of T2D, especially in patients with severe disease. A receiver operator characteristic curve was used to assess the accuracy of the IMS score using the area under the curve (AUC). The study cohort included 30 patients with complete baseline and long-term glycaemic data after their index DS-type surgery. Twelve patients (40%) were classified with severe T2D, and the distribution of IMS-based severity groups was similar between our cohort and the original IMS study (P = .42). IMS scores predicted long-term T2D remission with AUC = 0.77. Patients with IMS-based severe diabetes achieved significantly higher long-term remission after DS-type procedures compared with gastric bypass and/or sleeve gastrectomy from the original IMS study (42% vs. 12%; P < .05). The IMS score properly classifies the severity of T2D in our study cohort and adequately predicts its long-term remission after DS-type procedures. While T2D remission decreases with more severe IMS scores, long-term remission remains high after DS-type procedures among patients with severe disease.

Network meta-analysis for an ordinal outcome when outcome categorization varies across trials

BackgroundBinary outcomes are likely the most common in randomized controlled trials, but ordinal outcomes can also be of interest. For example, rather than simply collecting data on diseased versus healthy study subjects, investigators may collect information on the severity of disease, with no disease, mild, moderate, and severe disease as possible levels of the outcome. While some investigators may be interested in all levels of the ordinal variable, others may combine levels that are not of particular interest. Therefore, when research synthesizers subsequently conduct a network meta-analysis on a network of trials for which an ordinal outcome was measured, they may encounter a network in which outcome categorization varies across trials.MethodsThe standard method for network meta-analysis for an ordinal outcome based on a multinomial generalized linear model is not designed to accommodate the multiple outcome categorizations that might occur across trials. In this paper, we propose a network meta-analysis model for an ordinal outcome that allows for multiple categorizations. The proposed model incorporates the partial information provided by trials that combine levels through modification of the multinomial likelihoods of the affected arms, allowing for all available data to be considered in estimation of the comparative effect parameters. A Bayesian fixed effect model is used throughout, where the ordinality of the outcome is accounted for through the use of the adjacent-categories logit link.ResultsWe illustrate the method by analyzing a real network of trials on the use of antibiotics aimed at preventing liver abscesses in beef cattle and explore properties of the estimates of the comparative effect parameters through simulation. We find that even with the categorization of the levels varying across trials, the magnitudes of the biases are relatively small and that under a large sample size, the root mean square errors become small as well.ConclusionsOur proposed method to conduct a network meta-analysis for an ordinal outcome when the categorization of the outcome varies across trials, which utilizes the adjacent-categories logit link, performs well in estimation. Because the method considers all available data in a single estimation, it will be particularly useful to research synthesizers when the network of interest has only a limited number of trials for each categorization of the outcome.