Effect of Heat Input on Microstructure and Properties of Laser-Welded 316L/In601 Dissimilar Overlap Joints in High-Temperature Thermocouple.

Heat input, a crucial factor in the optimization of high-temperature thermocouple laser welding, has a significant impact on the appearance and mechanical properties of dissimilar welded joints involving stainless-steel- and nickel-based alloys. This study focuses on laser overlay welding of austenitic stainless steels and nickel-based alloys. The findings indicate that an increase in heat input has a more pronounced effect on the penetration depth and dilution rate. Under high heat input, the weld has cracks, spatter, and other defects. Additionally, considerable amounts of chromium (Cr) and nickel (Ni) elements are observed outside the grain near the crack, and their presence increases with higher heat input levels. Phase analysis reveals the presence of numerous Cr2Fe14C and Fe3Ni2 phases within the weld. The heat input increases to the range of 30-35 J/mm, and the weld changes from shear fracture to tensile fracture. In the center of the molten pool, the Vickers hardness is greater than that of the base metal, while in the fusion zone, the Vickers hardness is lower than that of the base metal. The overall hardness is in a downward trend with the increase of heat input, and the minimum hardness is only 159 HV0.3 at 40 J/mm. The heat input falls within the range of 28-30 J/mm, and the temperature shock resistance is at its peak.

Causal risk factor discovery for severe acute kidney injury using electronic health records.

BACKGROUND: Acute kidney injury (AKI), characterized by abrupt deterioration of renal function, is a common clinical event among hospitalized patients and it is associated with high morbidity and mortality. AKI is defined in three stages with stage-3 being the most severe phase which is irreversible. It is important to effectively discover the true risk factors in order to identify high-risk AKI patients and allow better targeting of tailored interventions. However, Stage-3 AKI patients are very rare (only 0.2% of AKI patients) with a large scale of features available in EHR (1917 potential risk features), yielding a scenario unfeasible for any correlation-based feature selection or modeling method. This study aims to discover the key factors and improve the detection of Stage-3 AKI. METHODS: A causal discovery method (McDSL) is adopted for causal discovery to infer true causal relationship between information buried in EHR (such as medication, diagnosis, laboratory tests, comorbidities and etc.) and Stage-3 AKI risk. The research approach comprised two major phases: data collection, and causal discovery. The first phase is propose to collect the data from HER (includes 358 encounters and 891 risk factors). Finally, McDSL is employed to discover the causal risk factors of Stage-3 AKI, and five well-known machine learning models are built for predicting Stage-3 AKI with 10-fold cross-validation (predictive accuracy were measured by AUC, precision, recall and F-score). RESULTS: McDSL is useful for further research of EHR. It is able to discover four causal features, all selected features are medications that are modifiable. The latest research of machine learning is employed to compare the performance of prediction, and the experimental result has verified the selected features are pivotal. CONCLUSIONS: The features selected by McDSL, which enable us to achieve significant dimension reduction without sacrificing prediction accuracy, suggesting potential clinical use such as helping physicians develop better prevention and treatment strategies.

Risk of hepatitis B reactivation in HBsAg-negative/HBcAb-positive patients with undetectable serum HBV DNA after treatment with rituximab for lymphoma: a meta-analysis.

BACKGROUND: Hepatitis B surface antigen (HBsAg)-negative/hepatitis B core antibody (HBcAb)-positive patients with undetectable serum hepatitis B virus (HBV) DNA have experienced and resolved hepatitis B virus (HBV) infection. Lymphoma patients with resolved HBV infection have high risk of HBV reactivation when treated with robust immunosuppressive agents, but the reported rate varies extensively between different studies. This study aims to estimate the risk of HBV reactivation in HBsAg-negative/HBcAb-positive patients receiving rituximab-containing chemotherapy for lymphoma. METHODS: Databases were searched for papers published in English until 8 August 2016. The pooled risk of HBV reactivation was estimated using a random-effects model. RESULTS: Data from 15 studies were retrieved, including a total of 1312 HBsAg-negative/HBcAb-positive lymphoma patients treated with rituximab-containing chemotherapy. The results revealed HBV reactivation rate of 9.0 % [95 % confidence interval (CI) 0.05-0.15]. In subgroup analysis, the reactivation rates for prospective and retrospective studies were 17 % (I 2 = 87.3 %; 95 % 0.08-0.39, p < 0.001) and 7 % (I 2 = 43.1 %; 95 % CI 0.05-0.11, p = 0.07), respectively. CONCLUSIONS: This meta-analysis confirms a measurable and potentially substantial risk of HBV reactivation in HBsAg-negative/HBcAb-positive patients with rituximab treatment for lymphoma. Prophylactic use of anti-HBV agents should be seriously considered for such patients.