Endemic melioidosis in central Taiwan-A longitudinal case cohort study.

Background: Melioidosis is a systemic and suppurative disease endemic in the Southeast Asia. In Taiwan, most cases are reported in the southern region and no relevant profiles have been reported in central region. In this study, we performed the epidemiologic and clinical analyses from the melioidosis cases in central Taiwan. Methods: The demographic, clinical, laboratory, radiologic, and outcome profiles were collected retrospectively and analyzed from patients whom Burkhoderia pseudomallei was isolated from clinical specimens during the 12-year study period (2011-2022). Results: Totally 11 melioidosis cases (10 males and 1 female) were diagnosed, among them only 2 (18.2%) cases lived in suburban areas. Seven (63.6%) cases were diagnosed during 2019-2020, and diabetes mellitus was the most relevant comorbidity (5, 45.4%). All cases presented with fever at arrival, but only 4 (36.4%) and 2 (18.2%) cases presented with dyspnea and shock, respectively. Pneumonitis and extrapulmonary involvement were found in 5 cases (45.4%) each. Appropriate empiric and targeted antibiotic treatments were found in 4 (36.4%) and 10 (91.0%) case, respectively. Two cases (18.2%) succumbed to infection despite appropriate treatment including targeted antibiotics. Conclusion: Melioidosis has become endemic in central Taiwan. Septic patients who present with suppurative or undetermined foci and have unsatisfied responses to standard treatment should arouse clinicians to take melioidosis into consideration.

A case series report on successful management of patients with COVID-19-associated lymphopenia and potential application of PG2.

Background: Lymphopenia and the resultant high neutrophil-to-lymphocyte ratio (NLR) are hallmark signs of severe COVID-19, and effective treatment remains unavailable. We retrospectively reviewed the outcomes of COVID-19 in a cohort of 26 patients admitted to Chung Shan Medical University Hospital (Taichung City, Taiwan). Twenty-five of the 26 patients recovered, including 9 patients with mild/moderate illness and 16 patients with severe/critical illness recovered. One patient died after refusing treatment. Case presentation: We report the cases of four patients with high NLRs and marked lymphopenia, despite receiving standard care. A novel injectable botanical drug, PG2, containing Astragalus polysaccharides, was administered to them as an immune modulator. The decrease in the NLR in these four patients was faster than that of other patients in the cohort (0.80 vs. 0.34 per day). Conclusion: All patients recovered from severe COVID-19 showed decreased NLR and normalized lymphocyte counts before discharge. Administration of PG2 may be of benefit to patients with moderate to severe COVID-19 and lymphopenia.

Machine learning using longitudinal prescription and medical claims for the detection of non-alcoholic steatohepatitis (NASH).

OBJECTIVES: To develop and evaluate machine learning models to detect patients with suspected undiagnosed non-alcoholic steatohepatitis (NASH) for diagnostic screening and clinical management. METHODS: In this retrospective observational non-interventional study using administrative medical claims data from 1 463 089 patients, gradient-boosted decision trees were trained to detect patients with likely NASH from an at-risk patient population with a history of obesity, type 2 diabetes mellitus, metabolic disorder or non-alcoholic fatty liver (NAFL). Models were trained to detect likely NASH in all at-risk patients or in the subset without a prior NAFL diagnosis (at-risk non-NAFL patients). Models were trained and validated using retrospective medical claims data and assessed using area under precision recall curves and receiver operating characteristic curves (AUPRCs and AUROCs). RESULTS: The 6-month incidences of NASH in claims data were 1 per 1437 at-risk patients and 1 per 2127 at-risk non-NAFL patients . The model trained to detect NASH in all at-risk patients had an AUPRC of 0.0107 (95% CI 0.0104 to 0.0110) and an AUROC of 0.84. At 10% recall, model precision was 4.3%, which is 60× above NASH incidence. The model trained to detect NASH in the non-NAFL cohort had an AUPRC of 0.0030 (95% CI 0.0029 to 0.0031) and an AUROC of 0.78. At 10% recall, model precision was 1%, which is 20× above NASH incidence. CONCLUSION: The low incidence of NASH in medical claims data corroborates the pattern of NASH underdiagnosis in clinical practice. Claims-based machine learning could facilitate the detection of patients with probable NASH for diagnostic testing and disease management.

Predicting misdiagnosed adult-onset type 1 diabetes using machine learning.

AIMS: It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to ineffective care. In this study a machine learning model was developed to identify type 1 diabetes patients misdiagnosed as type 2 diabetes. METHODS: In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Electronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical history. RESULTS: The model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in AEMR. CONCLUSIONS: This algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.

Modulated expression of human peripheral blood microRNAs from infancy to adulthood and its role in aging.

Accumulating evidence suggests a role for microRNAs (miRNAs) in regulating various processes of mammalian postnatal development and aging. To investigate the changes in blood-based miRNA expression from preterm infants to adulthood, we compared 365 miRNA expression profiles in a screening set of preterm infants and adults. Approximately one-third of the miRNAs were constantly expressed from postnatal development to adulthood, another one-third were differentially expressed between preterm infants and adults, and the remaining one-third were not detectable in these two groups. Based on their expression in infants and adults, the miRNAs were categorized into five classes, and six of the seven miRNAs chosen from each class except one with age-constant expression were confirmed in a validation set containing infants, children, and adults. Comparing the chromosomal locations of the different miRNA classes revealed two hot spots: the miRNA cluster on 14q32.31 exhibited age-constant expression, and the one on 9q22.21 exhibited up-regulation in adults. Furthermore, six miRNAs detectable in adults were down-regulated in older adults, and four chosen for individual quantification were verified in the validation set. Analysis of the network functions revealed that differentially regulated miRNAs between infants and adults and miRNAs that decreased during aging shared two network functions: inflammatory disease and inflammatory response. Four expression patterns existed in the 11 miRNAs from infancy to adulthood, with a significant transition in ages 9-20 years. Our results provide an overview on the regulation pattern of blood miRNAs throughout life and the possible biological functions performed by different classes of miRNAs.