Comparison of multiple linear regression and machine learning methods in predicting cognitive function in older Chinese type 2 diabetes patients.

INTRODUCTION: The prevalence of type 2 diabetes (T2D) has increased dramatically in recent decades, and there are increasing indications that dementia is related to T2D. Previous attempts to analyze such relationships principally relied on traditional multiple linear regression (MLR). However, recently developed machine learning methods (Mach-L) outperform MLR in capturing non-linear relationships. The present study applied four different Mach-L methods to analyze the relationships between risk factors and cognitive function in older T2D patients, seeking to compare the accuracy between MLR and Mach-L in predicting cognitive function and to rank the importance of risks factors for impaired cognitive function in T2D. METHODS: We recruited older T2D between 60-95 years old without other major comorbidities. Demographic factors and biochemistry data were used as independent variables and cognitive function assessment (CFA) was conducted using the Montreal Cognitive Assessment as an independent variable. In addition to traditional MLR, we applied random forest (RF), stochastic gradient boosting (SGB), Naïve Byer's classifier (NB) and eXtreme gradient boosting (XGBoost). RESULTS: Totally, the test cohort consisted of 197 T2D (98 men and 99 women). Results showed that all ML methods outperformed MLR, with symmetric mean absolute percentage errors for MLR, RF, SGB, NB and XGBoost respectively of 0.61, 0.599, 0.606, 0.599 and 0.2139. Education level, age, frailty score, fasting plasma glucose and body mass index were identified as key factors in descending order of importance. CONCLUSION: In conclusion, our study demonstrated that RF, SGB, NB and XGBoost are more accurate than MLR for predicting CFA score, and identify education level, age, frailty score, fasting plasma glucose, body fat and body mass index as important risk factors in an older Chinese T2D cohort.

Hydroxychloroquine might reduce risk of incident endometriosis in patients with systemic lupus erythematosus: A retrospective population-based cohort study.

BACKGROUND: SLE, which is common in women, is commonly treated with HCQ, an anti-inflammation medication. Reproductive-age women with SLE are prone to be impacted by endometriosis. This study analyzes the relationship between HCQ and endometriosis patients with SLE in order to determine whether HCQ is effective for treating the latter. METHODS: This population-based, retrospective cohort study analyzed the SLE risk in a cohort of newly diagnosed SLE patients with endometriosis during 2000 through 2013. Controls were selected at a 1:2 ratio through age-matching using the greedy algorithm. The Cox proportional hazard model was used to analyze the association between HCQ use and endometriosis incidence. Four different Cox regression models were used. Lastly, sensitivity analysis with PSOW and IPW was implemented to evaluate the hazard ratio (HR) of endometriosis after exposure with HCQ. RESULTS: In the cohort where age and sex matched high and low HCQ dosage, the average follow-up time was about 1 year. The cohort's overall incidence rates of endometriosis were 44.54 and 90.03 per 100000 person-month for high and low dosage respectively. The high dose group's conditional hazard ratio (aHR) for incidental endometriosis was 0.482 (CI = 0.191 to 1.213). The incidence rate and Kaplan-Meir curves of endometriosis were consistent with the results for the cohort. CONCLUSION: This study demonstrated that SLE patients continuously treated with HCQ have a lower risk of developing endometriosis. Clinically, HCQ can be beneficial for endometriosis patients with SLE.

Kansuinine A Ameliorates Atherosclerosis and Human Aortic Endothelial Cell Apoptosis by Inhibiting Reactive Oxygen Species Production and Suppressing IKKß/IκBα/NF-κB Signaling.

Reactive oxygen species (ROS)-induced vascular endothelial cell apoptosis is strongly associated with atherosclerosis progression. Herein, we aimed to examine whether Kansuinine A (KA), extracted from Euphorbia kansui L., prevents atherosclerosis development in a mouse model and inhibits cell apoptosis through oxidative stress reduction. Atherosclerosis development was analyzed in apolipoprotein E-deficient (ApoE-/-) mice fed a high-fat diet (HFD) using Oil Red O staining and H&E staining. Human aortic endothelial cells (HAECs) were treated with KA, followed by hydrogen peroxide (H2O2), to investigate the KA-mediated inhibition of ROS-induced oxidative stress and cell apoptosis. Oil Red O staining and H&E staining showed that atherosclerotic lesion size was significantly smaller in the aortic arch of ApoE-/- mice in the HFD+KA group than that in the aortic arch of those in the HFD group. Further, KA (0.1-1.0 µM) blocked the H2O2-induced death of HAECs and ROS generation. The H2O2-mediated upregulation of phosphorylated IKKß, phosphorylated IκBα, and phosphorylated NF-κB was suppressed by KA. KA also reduced the Bax/Bcl-2 ratio and cleaved caspase-3 expression, preventing H2O2-induced vascular endothelial cell apoptosis. Our results indicate that KA may protect against ROS-induced endothelial cell apoptosis and has considerable clinical potential in the prevention of atherosclerosis and cardiovascular diseases.

Plasma L5 levels are elevated in ischemic stroke patients and enhance platelet aggregation.

L5, the most electronegative and atherogenic subfraction of low-density lipoprotein (LDL), induces platelet activation. We hypothesized that plasma L5 levels are increased in acute ischemic stroke patients and examined whether lectin-like oxidized LDL receptor-1 (LOX-1), the receptor for L5 on endothelial cells and platelets, plays a critical role in stroke. Because amyloid ß (Aß) stimulates platelet aggregation, we studied whether L5 and Aß function synergistically to induce prothrombotic pathways leading to stroke. Levels of plasma L5, serum Aß, and platelet LOX-1 expression were significantly higher in acute ischemic stroke patients than in controls without metabolic syndrome (P < .01). In mice subjected to focal cerebral ischemia, L5 treatment resulted in larger infarction volumes than did phosphate-buffered saline treatment. Deficiency or neutralizing of LOX-1 reduced infarct volume up to threefold after focal cerebral ischemia in mice, illustrating the importance of LOX-1 in stroke injury. In human platelets, L5 but not L1 (the least electronegative LDL subfraction) induced Aß release via IκB kinase 2 (IKK2). Furthermore, L5+Aß synergistically induced glycoprotein IIb/IIIa receptor activation; phosphorylation of IKK2, IκBα, p65, and c-Jun N-terminal kinase 1; and platelet aggregation. These effects were blocked by inhibiting IKK2, LOX-1, or nuclear factor-κB (NF-κB). Injecting L5+Aß shortened tail-bleeding time by 50% (n = 12; P < .05 vs L1-injected mice), which was prevented by the IKK2 inhibitor. Our findings suggest that, through LOX-1, atherogenic L5 potentiates Aß-mediated platelet activation, platelet aggregation, and hemostasis via IKK2/NF-κB signaling. L5 elevation may be a risk factor for cerebral atherothrombosis, and downregulating LOX-1 and inhibiting IKK2 may be novel antithrombotic strategies.

Sesamol reduces the atherogenicity of electronegative L5 LDL in vivo and in vitro.

Highly electronegative low-density lipoprotein (LDL) L5 induces endothelial cell (EC) apoptosis, which leads to the development of atherosclerosis. We examined the effects of sesamol (1), a natural organic component of sesame oil, on plasma L5 levels and atherosclerosis development in a rodent model and on the L5-induced apoptosis of ECs. Syrian hamsters, which have an LDL profile similar to that of humans, were fed a normal chow diet (control), a high-fat diet (HFD), or a HFD supplemented with the administration of 50 or 100 mg/kg of 1 via oral gavage (HFD+1) for 16 weeks (n = 8 per group). Hamsters in the HFD+1 groups had reduced plasma L5 levels when compared with the HFD group. Oil Red O staining showed that atherosclerotic lesion size was markedly reduced in the aortic arch of hamsters in the HFD+1 groups when compared with that in the HFD group. In human aortic ECs, 0.3-3 µM 1 blocked L5-induced apoptosis in a dose-dependent manner. Further mechanistic studies showed that 1 inhibited the L5-induced lectin-like oxidized LDL receptor-1 (LOX-1)-dependent phosphorylation of p38 MAPK and activation of caspase-3 and increased phosphorylation of eNOS and Akt. Our findings suggest that sesamol (1) protects against atherosclerosis by reducing L5-induced atherogenicity.

The risk factors determined by four machine learning methods for the change of difference of bone mineral density in post-menopausal women after three years follow-up.

The prevalence of osteoporosis has drastically increased recently. It is not only the most frequent but is also a major global public health problem due to its high morbidity. There are many risk factors associated with osteoporosis were identified. However, most studies have used the traditional multiple linear regression (MLR) to explore their relationships. Recently, machine learning (Mach-L) has become a new modality for data analysis because it enables machine to learn from past data or experiences without being explicitly programmed and could capture nonlinear relationships better. These methods have the potential to outperform conventional MLR in disease prediction. In the present study, we enrolled a Chinese post-menopause cohort followed up for 4 years. The difference of T-score (δ-T score) was the dependent variable. Information such as demographic, biochemistry and life styles were the independent variables. Our goals were: (1) Compare the prediction accuracy between Mach-L and traditional MLR for δ-T score. (2) Rank the importance of risk factors (independent variables) for prediction of δ T-score. Totally, there were 1698 postmenopausal women were enrolled from MJ Health Database. Four different Mach-L methods namely, Random forest (RF), eXtreme Gradient Boosting (XGBoost), Naïve Bayes (NB), and stochastic gradient boosting (SGB), to construct predictive models for predicting δ-BMD after four years follow-up. The dataset was then randomly divided into an 80% training dataset for model building and a 20% testing dataset for model testing. A 10-fold cross-validation technique for hyperparameter tuning was used. The model with the lowest root mean square error for the validation dataset was viewed as the best model for each ML method. The averaged metrics of the RF, SGB, NB, and XGBoost models were used to compare the model performance of the benchmark MLR model that used the same training and testing dataset as the Mach-L methods. We defined that the priority demonstrated in each model ranked 1 as the most critical risk factor and 22 as the last selected risk factor. For Pearson correlation, age, education, BMI, HDL-C, and TSH were positively and plasma calcium level, and baseline T-score were negatively correlated with δ-T score. All four Mach-L methods yielded lower prediction errors than the MLR method and were all convincing Mach-L models. From our results, it could be noted that education level is the most important factor for δ-T Score, followed by DBP, smoking, SBP, UA, age, and LDL-C. All four Mach-L outperformed traditional MLR. By using Mach-L, the most important six risk factors were selected which are, from the most important to the least: DBP, SBP, UA, education level, TG and sleeping hour. δ T score was positively related to SBP, education level, UA and TG and negatively related to DBP and sleeping hour in postmenopausal Chinese women.

Changes in insulin resistance, glucose effectiveness, and first and second phases of insulin secretion in women aged 45-60 years old in Taiwan.

BACKGROUND: In women after menopause, the incidence of diabetes mellitus increases. Increased insulin resistance (IR), decreased glucose effectiveness (GE), and the first and second phases of insulin secretion (FPIS and SPIS), are the four most important factors that trigger glucose intolerance and diabetes (diabetogenic factor [DF]). In the cross-sectional study, we enrolled nondiabetic women between the ages of 45 and 60 years to observe the changes in DFs during the perimenopausal period and to elucidate the underlying mechanisms of diabetes in menopausal women. METHODS: We randomly enrolled 4194 women who underwent health checkups. Using demographic and biochemical data, IR, FPIS, SPIS, and GE were calculated using previously published equations. The relationship between the DFs and age was evaluated using a simple correlation. RESULTS: Body mass index, blood pressure, fasting plasma glucose, low-density lipoprotein cholesterol, triglyceride, and SPIS were higher, and GE was lower in older women (≥52 years old). A significant decrease in GE and increased SPIS were observed with age. However, no changes were observed in IR or FPIS. CONCLUSION: The IR and FPIS did not change during perimenopause. Increased SPIS may compensate for the decrease in GE, which is probably one of the reasons for the higher incidence of diabetes in menopausal women.

Using Machine Learning to Predict Abnormal Carotid Intima-Media Thickness in Type 2 Diabetes.

Carotid intima-media thickness (c-IMT) is a reliable risk factor for cardiovascular disease risk in type 2 diabetes (T2D) patients. The present study aimed to compare the effectiveness of different machine learning methods and traditional multiple logistic regression in predicting c-IMT using baseline features and to establish the most significant risk factors in a T2D cohort. We followed up with 924 patients with T2D for four years, with 75% of the participants used for model development. Machine learning methods, including classification and regression tree, random forest, eXtreme gradient boosting, and Naïve Bayes classifier, were used to predict c-IMT. The results showed that all machine learning methods, except for classification and regression tree, were not inferior to multiple logistic regression in predicting c-IMT in terms of higher area under receiver operation curve. The most significant risk factors for c-IMT were age, sex, creatinine, body mass index, diastolic blood pressure, and duration of diabetes, sequentially. Conclusively, machine learning methods could improve the prediction of c-IMT in T2D patients compared to conventional logistic regression models. This could have crucial implications for the early identification and management of cardiovascular disease in T2D patients.

Comparison between linear regression and four different machine learning methods in selecting risk factors for osteoporosis in a Chinese female aged cohort.

BACKGROUND: Population aging is emerging as an increasingly acute challenge for countries around the world. One particular manifestation of this phenomenon is the impact of osteoporosis on individuals and national health systems. Previous studies of risk factors for osteoporosis were conducted using traditional statistical methods, but more recent efforts have turned to machine learning approaches. Most such efforts, however, treat the target variable (bone mineral density [BMD] or fracture rate) as a categorical one, which provides no quantitative information. The present study uses five different machine learning methods to analyze the risk factors for T-score of BMD, seeking to (1) compare the prediction accuracy between different machine learning methods and traditional multiple linear regression (MLR) and (2) rank the importance of 25 different risk factors. METHODS: The study sample includes 24 412 women older than 55 years with 25 related variables, applying traditional MLR and five different machine learning methods: classification and regression tree, Naïve Bayes, random forest, stochastic gradient boosting, and eXtreme gradient boosting. The metrics used for model performance comparisons are the symmetric mean absolute percentage error, relative absolute error, root relative squared error, and root mean squared error. RESULTS: Machine learning approaches outperformed MLR for all four prediction errors. The average importance ranking of each factor generated by the machine learning methods indicates that age is the most important factor determining T-score, followed by estimated glomerular filtration rate (eGFR), body mass index (BMI), uric acid (UA), and education level. CONCLUSION: In a group of women older than 55 years, we demonstrated that machine learning methods provide superior performance in estimating T-Score, with age being the most important impact factor, followed by eGFR, BMI, UA, and education level.

Aging Affects Insulin Resistance, Insulin Secretion, and Glucose Effectiveness in Subjects with Normal Blood Glucose and Body Weight.

AIM: Several studies have demonstrated that factors including diabetes, including insulin resistance (IR), glucose effectiveness (GE), and the first and second phase of insulin secretion (FPIS, SPIS) could easily be calculated using basic characteristics and biochemistry profiles. Aging is accompanied by deteriorations of insulin resistance (IR) and insulin secretion. However, little is known about the roles of aging in the different phases of insulin secretion (ISEC), i.e., the first and second phase of insulin secretion (FPIS, SPIS), and glucose effectiveness (GE). METHODS: In total, 169 individuals (43 men and 126 women) recruited from the data bank of the Meei-Jaw (MJ) Health Screening Center and Cardinal Tien Hospital Data Access Center between 1999 and 2008, with a similar fasting plasma glucose (FPG: 90 mg/dL) and BMI (men: 23 kg/m2, women 22 kg/m2) were enrolled. The IR, FPIS, SPIS, and GE were estimated using our previously developed equations shown below. Pearson correlation analysis was conducted to assess the correlations between age and four diabetes factors (DFs: IR, FPIS, SPIS, and GE). The equations that are used to calculate the DF in the present study were built and published by our group. RESULTS: The age of the participants ranged from 18 to 78 years. Men had higher FPIS but lower HDL-C levels than women (2.067 ± 0.159, 1.950 ± 0.186 µU/min and 1.130 ± 0.306, 1.348 ± 0.357 mmol/dl, accordingly). The results of the Pearson correlation revealed that age was negatively related to the IR and GE in both genders (IR: r = -0.39, p < 0.001 for men, r = -0.24, p < 0.003 for women; GE: r = 0.66, p < 0.001 for men, r = 0.78, p < 0.001 for women). At the same time, the FPIS was also only found to be negatively correlated with age in females (r = -0.238, p = 0.003), but there was no difference in the SPIS and age among both genders. CONCLUSIONS: We have found that in Chinese subjects with a normal FPG level (90 mg/dL) and body mass index (men: 23 kg/m2, women: 22: kg/m2), age is negatively related to the IR and GE among both genders. Only the FPIS was found to be negatively related to age in women. The tightness of their relationships, from the highest to the lowest, are GE, FPIS, and IR. These results should be interpreted with caution because of the small sample size.

Comparison between multiple logistic regression and machine learning methods in prediction of abnormal thallium scans in type 2 diabetes.

BACKGROUND: The prevalence of type 2 diabetes (T2D) has been increasing dramatically in recent decades, and 47.5% of T2D patients will die of cardiovascular disease. Thallium-201 myocardial perfusion scan (MPS) is a precise and non-invasive method to detect coronary artery disease (CAD). Most previous studies used traditional logistic regression (LGR) to evaluate the risks for abnormal CAD. Rapidly developing machine learning (Mach-L) techniques could potentially outperform LGR in capturing non-linear relationships. AIM: To aims were: (1) Compare the accuracy of Mach-L methods and LGR; and (2) Found the most important factors for abnormal TMPS. METHODS: 556 T2D were enrolled in the study (287 men and 269 women). Demographic and biochemistry data were used as independent variables and the sum of stressed score derived from MPS scan was the dependent variable. Subjects with a MPS score ≥ 9 were defined as abnormal. In addition to traditional LGR, classification and regression tree (CART), random forest, Naïve Bayes, and eXtreme gradient boosting were also applied. Sensitivity, specificity, accuracy and area under the receiver operation curve were used to evaluate the respective accuracy of LGR and Mach-L methods. RESULTS: Except for CART, the other Mach-L methods outperformed LGR, with gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking emerging as the most important factors to predict abnormal MPS. CONCLUSION: Four Mach-L methods are found to outperform LGR in predicting abnormal TMPS in Chinese T2D, with the most important risk factors being gender, body mass index, age, low-density lipoprotein cholesterol, glycated hemoglobin and smoking.

Gamma-Glutamyltransferase Is a Predictor for Future Changes of Diabetogenic Factors in Aged Chinese-A Four-Year Follow-Up Study.

Glucose homeostasis in the body is determined by four diabetes factors (DFs): insulin resistance (IR), glucose effectiveness (GE), and the two phases of insulin secretion-first phase (FPIS) and second phase (SPIS). Previous research points to a correlation between elevated levels of gamma-glutamyl transferase (γGT) and an increased risk of type 2 diabetes. This study investigates the relationship between γGT and the four DFs in older Chinese individuals. This study involved 2644 men and 2598 women, all of whom were relatively healthy Chinese individuals aged 60 years or more. The DFs were calculated using formulas developed by our research, based on demographic data and factors related to metabolic syndrome. Pearson's correlation was utilized to assess the relationship between γGT and the four DFs. The findings suggested a positive correlation between γGT and IR, FPIS, and SPIS, but a negative correlation with GE in men. Among women, only SPIS and GE were significantly correlated with γGT. The factors showed varying degrees of correlation, listed in descending order as follows: GE, SPIS, FPIS, and IR. This study confirms a significant correlation between γGT and DFs in this population, highlighting the noteworthy role of GE.

Electronegative low-density lipoprotein induces cardiomyocyte apoptosis indirectly through endothelial cell-released chemokines.

Cardiomyocyte apoptosis has a critical role in the pathogenesis of heart failure. L5, the most negatively charged subfraction of human plasma low-density lipoprotein (LDL), induces several atherogenic responses in endothelial cells (ECs), including apoptosis. We hypothesized that L5 also contributes to cardiomyocyte apoptosis and studied whether it does so indirectly by inducing the secretion of factors from ECs. We examined apoptosis of rat cardiomyocytes treated with culture-conditioned medium (CCM) of rat ECs that were exposed to L5 or L1 (the least negatively charged LDL subfraction). Apoptosis at early and late time points was twofold greater in cardiomyocytes treated with L5 CCM than in those treated with L1 CCM. The indirect effect of L5 on cardiomyocyte apoptosis was significantly reduced by pretreating ECs with inhibitors of phosphatidylinositol 3-kinase (PI3K) or CXC receptor 2 (CXCR2). Studies with cytokine protein arrays revealed that L5 CCM, but not L1 CCM, contained high levels of ELR(+) CXC chemokines, including lipopolysaccharide-induced chemokine (LIX) and interleukin (IL)-8. The L5-induced release of these chemokines from ECs was abolished by inhibiting the lectin-like oxidized LDL receptor-1 (LOX-1). Addition of recombinant LIX or IL-8 to CCM-free cardiomyocyte cultures increased apoptosis and enhanced production of tumor necrosis factor (TNF)-α and IL-1ß by increasing the translocation of NF-κB into the nucleus; these effects were attenuated by inhibiting PI3K and CXCR2. In conclusion, L5 may indirectly induce cardiomyocyte apoptosis by enhancing secretion of ELR(+) CXC chemokines from ECs, which in turn activate CXCR2/PI3K/NF-κB signaling to increase the release of TNF-α and IL-1ß.

Sesamol Attenuates Renal Inflammation and Arrests Reactive-Oxygen-Species-Mediated IL-1ß Secretion via the HO-1-Induced Inhibition of the IKKα/NFκB Pathway In Vivo and In Vitro.

Chronic nephritis leads to irreversible renal fibrosis, ultimately leading to chronic kidney disease (CKD) and death. Macrophage infiltration and interleukin 1ß (IL-1ß) upregulation are involved in inflammation-mediated renal fibrosis and CKD. Sesamol (SM), which is extracted from sesame seeds, has antioxidant and anti-inflammatory properties. We aimed to explore whether SM mitigates macrophage-mediated renal inflammation and its underlying mechanisms. ApoE-/- mice were subjected to 5/6 nephrectomy (5/6 Nx) with or without the oral gavage of SM for eight weeks. Blood and urine samples and all the kidney remnants were collected for analysis. Additionally, THP-1 cells were used to explore the mechanism through which SM attenuates renal inflammation. Compared with the sham group, the 5/6 Nx ApoE-/- mice exhibited a significant increase in the macrophage infiltration of the kidneys (nephritis), upregulation of IL-1ß, generation of reactive oxygen species, reduced creatinine clearance, and renal fibrosis. However, the administration of SM significantly alleviated these effects. SM suppressed the H2O2-induced secretion of IL-1ß from the THP-1 cells via the heme oxygenase-1-induced inhibition of the IKKα-NF-κB pathway. SM attenuated renal inflammation and arrested macrophage accumulation by inhibiting IKKα, revealing a novel mechanism of the therapeutic effects of SM on renal injury and offering a potential approach to CKD treatment.

Apolipoprotein C3-Rich Low-Density Lipoprotein Induces Endothelial Cell Senescence via FBXO31 and Its Inhibition by Sesamol In Vitro and In Vivo.

Premature endothelial senescence decreases the atheroprotective capacity of the arterial endothelium. Apolipoprotein C3 (ApoC3) delays the catabolism of triglyceride-rich particles and plays a critical role in atherosclerosis progression. FBXO31 is required for the intracellular response to DNA damage, which is a significant cause of cellular senescence. Sesamol is a natural antioxidant with cardiovascular-protective properties. In this study, we aimed to examine the effects of ApoC3-rich low-density lipoprotein (AC3RL) mediated via FBXO31 on endothelial cell (EC) senescence and its inhibition by sesamol. AC3RL and ApoC3-free low-density lipoproteins (LDL) (AC3(-)L) were isolated from the plasma LDL of patients with ischemic stroke. Human aortic endothelial cells (HAECs) treated with AC3RL induced EC senescence in a dose-dependent manner. AC3RL induced HAEC senescence via DNA damage. However, silencing FBXO31 attenuated AC3RL-induced DNA damage and reduced cellular senescence. Thus, FBXO31 may be a novel therapeutic target for endothelial senescence-related cardiovascular diseases. Moreover, the aortic arch of hamsters fed a high-fat diet with sesamol showed a substantial reduction in their atherosclerotic lesion size. In addition to confirming the role of AC3RL in aging and atherosclerosis, we also identified AC3RL as a potential therapeutic target that can be used to combat atherosclerosis and the onset of cardiovascular disease in humans.

The Effect of Schwann Cells/Schwann Cell-Like Cells on Cell Therapy for Peripheral Neuropathy.

Peripheral neuropathy is a common neurological issue that leads to sensory and motor disorders. Over time, the treatment for peripheral neuropathy has primarily focused on medications for specific symptoms and surgical techniques. Despite the different advantages of these treatments, functional recovery remains less than ideal. Schwann cells, as the primary glial cells in the peripheral nervous system, play crucial roles in physiological and pathological conditions by maintaining nerve structure and functions and secreting various signaling molecules and neurotrophic factors to support both axonal growth and myelination. In addition, stem cells, including mesenchymal stromal cells, skin precursor cells and neural stem cells, have the potential to differentiate into Schwann-like cells to perform similar functions as Schwann cells. Therefore, accumulating evidence indicates that Schwann cell transplantation plays a crucial role in the resolution of peripheral neuropathy. In this review, we summarize the literature regarding the use of Schwann cell/Schwann cell-like cell transplantation for different peripheral neuropathies and the potential role of promoting nerve repair and functional recovery. Finally, we discuss the limitations and challenges of Schwann cell/Schwann cell-like cell transplantation in future clinical applications. Together, these studies provide insights into the effect of Schwann cells/Schwann cell-like cells on cell therapy and uncover prospective therapeutic strategies for peripheral neuropathy.

Comparing Multiple Linear Regression and Machine Learning in Predicting Diabetic Urine Albumin-Creatinine Ratio in a 4-Year Follow-Up Study.

The urine albumin-creatinine ratio (uACR) is a warning for the deterioration of renal function in type 2 diabetes (T2D). The early detection of ACR has become an important issue. Multiple linear regression (MLR) has traditionally been used to explore the relationships between risk factors and endpoints. Recently, machine learning (ML) methods have been widely applied in medicine. In the present study, four ML methods were used to predict the uACR in a T2D cohort. We hypothesized that (1) ML outperforms traditional MLR and (2) different ranks of the importance of the risk factors will be obtained. A total of 1147 patients with T2D were followed up for four years. MLR, classification and regression tree, random forest, stochastic gradient boosting, and eXtreme gradient boosting methods were used. Our findings show that the prediction errors of the ML methods are smaller than those of MLR, which indicates that ML is more accurate. The first six most important factors were baseline creatinine level, systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose. In conclusion, ML might be more accurate in predicting uACR in a T2D cohort than the traditional MLR, and the baseline creatinine level is the most important predictor, which is followed by systolic and diastolic blood pressure, glycated hemoglobin, and fasting plasma glucose in Chinese patients with T2D.

Comparison between Machine Learning and Multiple Linear Regression to Identify Abnormal Thallium Myocardial Perfusion Scan in Chinese Type 2 Diabetes.

Type 2 diabetes mellitus (T2DM) patients have a high risk of coronary artery disease (CAD). Thallium-201 myocardial perfusion scan (Th-201 scan) is a non-invasive and extensively used tool in recognizing CAD in clinical settings. In this study, we attempted to compare the predictive accuracy of evaluating abnormal Th-201 scans using traditional multiple linear regression (MLR) with four machine learning (ML) methods. From the study, we can determine whether ML surpasses traditional MLR and rank the clinical variables and compare them with previous reports.In total, 796 T2DM, including 368 men and 528 women, were enrolled. In addition to traditional MLR, classification and regression tree (CART), random forest (RF), stochastic gradient boosting (SGB) and eXtreme gradient boosting (XGBoost) were also used to analyze abnormal Th-201 scans. Stress sum score was used as the endpoint (dependent variable). Our findings show that all four root mean square errors of ML are smaller than with MLR, which implies that ML is more precise than MLR in determining abnormal Th-201 scans by using clinical parameters. The first seven factors, from the most important to the least are:body mass index, hemoglobin, age, glycated hemoglobin, Creatinine, systolic and diastolic blood pressure. In conclusion, ML is not inferior to traditional MLR in predicting abnormal Th-201 scans, and the most important factors are body mass index, hemoglobin, age, glycated hemoglobin, creatinine, systolic and diastolic blood pressure. ML methods are superior in these kinds of studies.

Mechanism and kinetics of pore formation in membranes by water-soluble amphipathic peptides.

How antimicrobial peptides form pores in membranes is of interest as a fundamental membrane process. However, the underlying molecular mechanism, which has potential applications in therapeutics, nonviral gene transfer, and drug delivery, has been in dispute. We have resolved this mechanism by observing the time-dependent process of pore formation in individual giant unilamellar vesicles (GUVs) exposed to a melittin solution. An individual GUV first expanded its surface area at constant volume and then suddenly reversed to expanding its volume at constant area. The area expansion, the volume expansion, and the point of reversal all match the results of equilibrium measurements performed on peptide-lipid mixtures. The mechanism includes a negative feedback that makes peptide-induced pores stable with a well defined size, contrary to the suggestion that peptides disintegrate the membrane in a detergent-like manner.