OBJECTIVE: To determine whether inflammatory biomarkers are causal risk factors for more myopic refractive errors.
METHODS: Northern Sweden Population Health Study (NSPHS), providing inflammatory biomarkers data; UK Biobank, providing refractive errors data. 95,619 European men and women aged 40 to 69 years with available information of refractive errors and inflammatory biomakers. Inflammatory biomarkers including ADA, CCL23, CCL25, CD6, CD40, CDCP-1, CST5, CXCL-5, CXCL-6, CXCL-10, IL-10RB, IL-12B, IL-15RA, IL-18R1, MCP-2, MMP-1, TGF-β1, TNF-β, TWEAK and VEGF-A were exposures, and spherical equivalent (SE) using the formula SE = sphere + (cylinder/2) was outcome.
RESULTS: Mendelian randomization analyses showed that each unit increase in VEGF-A, CD6, MCP-2 were causally related to a more myopic refractive errors of 0.040 D/pg.mL-1 (95% confidence interval 0.019 to 0.062; P = 2.031 × 10-4), 0.042 D/pg.mL-1 (0.027 to 0.057; P = 7.361 × 10-8) and 0.016 D/pg.mL-1 (0.004 to 0.028; P = 0.009), and each unit increase in TWEAK was causally related to a less myopic refractive errors of 0.104 D/pg.mL-1 (-0.152 to -0.055; P = 2.878 × 10-5). Tested by the MR-Egger, weighted median, MR-PRESSO, Leave-one-out methods, our results were robust to horizontal pleiotropy and heterogeneity in VEGF-A, MCP-2, CD6, but not in TWEAK.
CONCLUSIONS: Our Mendelian Randomization analysis supported the causal effects of VEGF-A, MCP-2, CD6 and TWEAK on myopic refractive errors. These findings are important for providing new indicators for early intervention of myopia to make myopic eyesight threatening consequences less inevitable.

UNASSIGNED: Our study investigated the potential of peripheral blood T cell CD25, CD28, and CTLA-4 gene transcription levels as predictive biomarkers for acute graft-versus-host disease (aGVHD) following allogeneic hematopoietic stem cell transplantation (allo-HSCT).
UNASSIGNED: Real-time reverse transcription fluorescent quantitative PCR (RT-qPCR) analysis was conducted on day +7, +14, and +21 post-transplantation in patients undergoing allo-HSCT.
UNASSIGNED: Elevated levels of CD25 and CTLA-4 mRNA were found to be associated with the occurrence of aGVHD, as well as severe and gastrointestinal aGVHD. Receiver operating characteristic (ROC) curve analysis was utilized to assess the predictive value of each biomarker. Combined analysis of CD25 and CTLA-4 mRNA levels demonstrated promising predictive potential for aGVHD.
UNASSIGNED: Our results confirmed that the transcription levels of CD25 and CTLA-4 genes could be used as early predictive biomarkers for aGVHD post-allo-HSCT.

OBJECTIVE: The concentration of the placental circulating factor in early pregnancy is often extremely low, and the traditional prediction method cannot meet the clinical demand for early detection preeclampsia in high-risk gravida. It is of prime importance to seek an ultra-sensitive early prediction method.
METHODS: In this study, finite-different time-domain (FDTD) and Discrete Dipole Approximation (DDA) simulation, and electron beam lithography (EBL) methods were used to develop a bowtie nanoantenna (BNA) with the best field enhancement and maximum coupling efficiency. Bio-modification of the placental circulating factor (sFlt-1, PLGF) to the noble nanoparticles based on the amino coupling method were explored. A BNA LSPR biosensor which can specifically identify the placental circulating factor in preeclampsia was constructed.
RESULTS: The BNA LSPR biosensor can detect serum placental circulating factors without toxic labeling. Serum sFlt-1 extinction signal (Δλmax) in the preeclampsia group was higher than that in the normal pregnancy group (14.37 ± 2.56 nm vs. 4.21 ± 1.36 nm), p = 0.008, while the serum PLGF extinction signal in the preeclampsia group was lower than that in the normal pregnancy group (5.36 ± 3.15 nm vs. 11.47 ± 4.92 nm), p = 0.013. The LSPR biosensor detection results were linearly consistent with the ELISA kit.
CONCLUSIONS: LSPR biosensor based on BNA can identify the serum placental circulating factor of preeclampsia with high sensitivity, without toxic labeling and with simple operation, and it is expected to be an early detection method for preeclampsia.

BACKGROUND: To investigate the relationship between cord blood levels of Angiopoietin-1 (Ang-1) and S-endoglin (sCD105) and bronchopulmonary dysplasia (BPD) in preterm infants.
METHODS: Sixty-one preterm infants admitted to the neonatal intensive care unit of the study hospital between July 2021 and September 2022 were included. Cord blood was collected after the birth of premature infants. Ang-1 and sCD105 levels were quantified using the vascular endothelial growth factor enzyme-linked immunosorbent assay. Preterm infants were divided into BPD and non-BPD groups, and differences in Ang-1 and sCD105 levels between the two groups were compared. A binary logistic model was used to assess the association between low and high levels Ang-1 and BPD in preterm infants.
RESULTS: In the study, there were 20 preterm infants with BPD (32.8%) and 41 preterm infants with non-BPD (67.2%). Ang-1 concentration levels were lower in the BPD group than in the non-BPD group (7105.43 (5617.01-8523.00) pg/ml vs. 10488.03 (7946.19-15962.77) pg/ml, P = 0.027). However, the sCD105 concentration levels were not significantly different between the BPD and non-BPD groups (P = 0.246). A median Ang-1 concentration of 8800.40 pg/ml was calculated. Logistic regression analysis showed that after adjusting for gestational age, birth weight, and maternal prenatal steroid hormone application, the odds ratio (OR) was 8.577 for the risk of BPD in preterm infants with Ang-1 concentrations of ≤ 8800.40 pg/ml compared to those with Ang-1 concentrations of > 8800.40 pg/ml (OR: 8.577, 95% confidence interval: 1.265-58.155, P = 0.028).
CONCLUSIONS: Our study indicated that Ang-1 levels in the cord blood of preterm infants may be associated the risk of BPD. In the future, we will continue to conduct study with large samples.

Gestational Diabetes Mellitus (GDM) poses significant health risks to mothers and infants. Early prediction and effective management are crucial to improving outcomes. Machine learning techniques have emerged as powerful tools for GDM prediction. This review compiles and analyses the available studies to highlight key findings and trends in the application of machine learning for GDM prediction. A comprehensive search of relevant studies published between 2000 and September 2023 was conducted. Fourteen studies were selected based on their focus on machine learning for GDM prediction. These studies were subjected to rigorous analysis to identify common themes and trends. The review revealed several key themes. Models capable of predicting GDM risk during the early stages of pregnancy were identified from the studies reviewed. Several studies underscored the necessity of tailoring predictive models to specific populations and demographic groups. These findings highlighted the limitations of uniform guidelines for diverse populations. Moreover, studies emphasised the value of integrating clinical data into GDM prediction models. This integration improved the treatment and care delivery for individuals diagnosed with GDM. While different machine learning models showed promise, selecting and weighing variables remains complex. The reviewed studies offer valuable insights into the complexities and potential solutions in GDM prediction using machine learning. The pursuit of accurate, early prediction models, the consideration of diverse populations, clinical data, and emerging data sources underscore the commitment of researchers to improve healthcare outcomes for pregnant individuals at risk of GDM.

Mastitis is a global production disease that needs an intelligent solution to tackle effectively. Infrared Thermography (IRT) is a non-invasive technology that could be incorporated into routine day-to-day farm activities to monitor the health status of the animals. In this study, the udder health status was routinely monitored for 30 days among 41 Murrah buffaloes via IRT and the California Mastitis Test (CMT). Further, somatic cell count (SCC), microbial identification, and milk quality parameters were also estimated for representative samples. The thermal imaging data obtained was tabulated and back propagated from the 0th day to the -10th day and front propagated from the 0th day to +10th day for all the udder quarters. Results revealed that on the 0th day, the mean of udder skin surface temperature (USST) and teat skin surface temperature (TSST) showed a difference (p < 0.05) in the sub-clinical mastitis (SCM) and clinical mastitis (CM) affected quarters to the healthy quarters, and their degree of difference was the highest. The indication of infection was signaled during the -9th to -5th day to the 0th day in SCM and CM cases. There was a steep increment in the temperature from -2nd and -1st day to the 0th day of infection. Sometimes, some quarters show an increment in temperature due to mastitis during morning hours but recover by evening milking due to the animal\'s innate immune system. Thus, the initiation period in which the udder gets assaulted is crucial in the early assessment of SCM by monitoring temperature change using IRT.

UNASSIGNED: Early detection of hyperlipidemic acute pancreatitis (HLAP) with exacerbation tendency is crucial for clinical decision-making and improving prognosis. The aim of this study was to establish a reliable model for the early prediction of HLAP severity.
UNASSIGNED: A total of 225 patients with first-episode HLAP who were admitted to Fujian Medical University Union Hospital from June 2012 to June 2023 were included. Patients were divided into mild acute pancreatitis (MAP) or moderate-severe acute pancreatitis and severe acute pancreatitis (MSAP+SAP) groups. Independent predictors for progression to MSAP or SAP were identified through univariate analysis and least absolute shrinkage and selection operator regression. A nomogram was established through multivariate logistic regression analysis to predict this progression. The calibration, receiver operating characteristic(ROC), and clinical decision curves were employed to evaluate the model\'s consistency, differentiation, and clinical applicability. Clinical data of 93 patients with first-episode HLAP who were admitted to the First Affiliated Hospital of Fujian Medical University from October 2015 to October 2022 were collected for external validation.
UNASSIGNED: White blood cell count, lactate dehydrogenase, albumin, serum creatinine, serum calcium, D-Dimer were identified as independent predictors for progression to MSAP or SAP in patients with HLAP and used to establish a predictive nomogram. The internally verified Harrell consistency index (C-index) was 0.908 (95% CI 0.867-0.948) and the externally verified C-index was 0.950 (95% CI 0.910-0.990). The calibration, ROC, and clinical decision curves showed this nomogram\'s good predictive ability.
UNASSIGNED: We have established a nomogram that can help identify HLAP patients who are likely to develop MSAP or SAP at an early stage, with high discrimination and accuracy.

UNASSIGNED: Type 3 cardiorenal syndrome (CRS type 3) triggers acute cardiac injury from acute kidney injury (AKI), raising mortality in AKI patients. We aimed to identify risk factors for CRS type 3 and develop a predictive nomogram.
UNASSIGNED: In this retrospective study, 805 AKI patients admitted at the Department of Nephrology, Second Hospital of Shanxi Medical University from 1 January 2017, to 31 December 2021, were categorized into a study cohort (406 patients from 2017.1.1-2021.6.30, with 63 CRS type 3 cases) and a validation cohort (126 patients from 1 July 2021 to 31 Dec 2021, with 22 CRS type 3 cases). Risk factors for CRS type 3, identified by logistic regression, informed the construction of a predictive nomogram. Its performance and accuracy were evaluated by the area under the curve (AUC), calibration curve and decision curve analysis, with further validation through a validation cohort.
UNASSIGNED: The nomogram included 6 risk factors: age (OR = 1.03; 95%CI = 1.009-1.052; p = 0.006), cardiovascular disease (CVD) history (OR = 2.802; 95%CI = 1.193-6.582; p = 0.018), mean artery pressure (MAP) (OR = 1.033; 95%CI = 1.012-1.054; p = 0.002), hemoglobin (OR = 0.973; 95%CI = 0.96--0.987; p < 0.001), homocysteine (OR = 1.05; 95%CI = 1.03-1.069; p < 0.001), AKI stage [(stage 1: reference), (stage 2: OR = 5.427; 95%CI = 1.781-16.534; p = 0.003), (stage 3: OR = 5.554; 95%CI = 2.234-13.805; p < 0.001)]. The nomogram exhibited excellent predictive performance with an AUC of 0.907 in the study cohort and 0.892 in the validation cohort. Calibration and decision curve analyses upheld its accuracy and clinical utility.
UNASSIGNED: We developed a nomogram predicting CRS type 3 in AKI patients, incorporating 6 risk factors: age, CVD history, MAP, hemoglobin, homocysteine, and AKI stage, enhancing early risk identification and patient management.

The extent to which early weight loss in behavioral weight control interventions predicts long-term success remains unclear. In this study, we developed an algorithm aimed at classifying weight change trajectories and examined its ability to predict long-term weight loss based on weight early change. We utilized data from 667 de-identified individuals who participated in a commercial weight loss program (Instinct Health Science), comprising 69,363 weight records. Sequential polynomial regression models were employed to classify participants into distinct weight trajectory patterns based on key model parameters. Next, we applied multinomial logistic models to evaluate if early weight loss in the first 14 days and prolonged duration of participation were significantly associated with long-term weight loss patterns. The mean percentage of weight loss was 7.9 ± 5.1% over 133 ± 69 days. Our analysis revealed four main weight loss trajectory patterns: a steady decrease over time (30.6%), a decrease to a plateau with subsequent decline (15.8%), a decrease to a plateau with subsequent increase (46.9%), and no substantial decrease (6.7%). Early weight change rate and total participating duration emerged as significant factors in differentiating long-term weight loss patterns. These findings contribute to support the provision of tailored advice in the early phase of behavioral interventions for weight loss.

OBJECTIVE: To build and validate an early risk prediction model for gestational diabetes mellitus (GDM) based on first-trimester electronic medical records including maternal demographic and clinical risk factors.
METHODS: To develop and validate a GDM prediction model, two datasets were used in this retrospective study. One included data of 14,015 pregnant women from Máxima Medical Center (MMC) in the Netherlands. The other was from an open-source database nuMoM2b including data of 10,038 nulliparous pregnant women, collected in the USA. Widely used maternal demographic and clinical risk factors were considered for modeling. A GDM prediction model based on elastic net logistic regression was trained from a subset of the MMC data. Internal validation was performed on the remaining MMC data to evaluate the model performance. For external validation, the prediction model was tested on an external test set from the nuMoM2b dataset.
RESULTS: An area under the receiver-operating-characteristic curve (AUC) of 0.81 was achieved for early prediction of GDM on the MMC test data, comparable to the performance reported in previous studies. While the performance markedly decreased to an AUC of 0.69 when testing the MMC-based model on the external nuMoM2b test data, close to the performance trained and tested on the nuMoM2b dataset only (AUC = 0.70).