Background. Frozen shoulder (FS) is associated with pain, reduced range of motion (ROM), and shoulder function. The condition occurs in 2–5% of the population, and it is especially common around the age of 50 years. FS symptoms will recover after 1–4 years. Many patients turn to acupuncture in order to alleviate the FS symptoms. Objective. In this review, we will investigate the efficiency of acupuncture as a FS treatment. Methods. A literature search of acupuncture and FS‐related keywords was performed in the following databases: PubMed, Cochrane Library, Embase, and Web of Science. Thirteen publications were included for a systematic review, and a meta‐analysis was done using the following measurements: visual analogue scale (VAS) for pain, Constant‐Murley Shoulder Outcome Score (CMS) for shoulder function, and active shoulder ROM including flexion, abduction, and external rotation. The Cochrane Collaboration’s risk of bias tool and quality of evidence GRADE recommendations and STRICTA 2010 were used to grade the included publications. Results. A meta‐analysis on VAS pain score showed significant pain reduction, restoring CMS shoulder function, and flexion ROM in favor of acupuncture versus the control. In external rotation and abduction ROM, a meta‐analysis was not significant. The most used acupoints are Jian Yu (LI15) and Jian Liao (TB14). Conclusions. The results indicate that acupuncture could be safe and effective for pain reduction, restoring shoulder function, and restoring flexion ROM for FS patients in the short term and midterm. However, the level of evidence was very low. More high‐quality and longer studies are needed in order to robust the evidence.

Background: Mucin 4 (MUC4) plays an important role in protecting and lubricating the epithelial surface of reproductive tracts, but its role in the pathogenesis of endometriosis is largely unknown. Methods: To correlate MUC4 polymorphism with the risk of endometriosis and endometriosis-related infertility, we performed a case-control study of 140 patients and 150 healthy women. Six unique single-nucleotide polymorphisms (SNPs) (rs882605, rs1104760, rs2688513, rs2246901, rs2258447 and rs2291652) were selected for this study. DNA fragments containing the target SNP sites were amplified by polymerase chain reaction using the TaqMan SNP Genotyping Assay System to evaluate allele frequency and distribution of genotype in MUC4 polymorphisms. Results: Both the T/G genotype of rs882605 and the frequency of haplotype T-T (rs882605 and rs1104760) were higher in patients than in controls and were statistically significant. The frequency of the C allele at rs1104760, the C allele at rs2688513, the G allele at rs2246901 and the A allele at rs2258447 were associated with advanced stage of endometriosis. Moreover, the G allele at rs882605 was verified as a key genetic factor for infertility in patients. Protein sequence analysis indicated that amino acid substitutions by genetic variations at rs882605, rs2688513 and rs2246901 occur in the putative functional loops and the type D von Willebrand factor (VWFD) domain in the MUC4 sequence. Conclusions: MUC4 polymorphisms are associated with endometriosis development and endometriosis-related infertility in the Taiwanese population.

Alzheimer’s disease (AD) is a neurodegenerative disorder causing 70% of dementia cases. However, the mechanism of disease development is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of AD's mechanism from machine learning (ML) is so far unrealized, majorly due to the lack of needed data density. To harness the AD mechanism's knowledge from the expression profiles of postmortem prefrontal cortex samples of 310 AD and 157 controls, we used seven predictive operators or combinations of RapidMiner Studio operators to establish predictive models from the input matrix and to assign a weight to each attribute. Besides, conventional fold-change methods were also applied as controls. The identified genes were further submitted to enrichment analysis for KEGG pathways. The average accuracy of ML models ranges from 86.30% to 91.22%. The overlap ratio of the identified genes between ML and conventional methods ranges from 19.7% to 21.3%. ML exclusively identified oxidative phosphorylation genes in the AD pathway. Our results highlighted the deficiency of oxidative phosphorylation in AD and suggest that ML should be considered as complementary to the conventional fold-change methods in transcriptome studies.

Background: Huntington’s disease (HD) is an inherited disorder caused by the polyglutamine (poly-Q) mutations of the HTT gene results in neurodegeneration characterized by chorea, loss of coordination, cognitive decline. However, HD pathogenesis is still elusive. Despite the availability of a wide range of biological data, a comprehensive understanding of HD’s mechanism from machine learning is so far unrealized, majorly due to the lack of needed data density.Methods: To harness the knowledge of the HD pathogenesis from the expression profiles of postmortem prefrontal cortex samples of 157 HD and 157 controls, we used gene profiling ranking as the criteria to reduce the dimension to the order of magnitude of the sample size, followed by machine learning using the decision tree, rule induction, random forest, and generalized linear model.Results: These four Machine learning models identified 66 potential HD-contributing genes, with the cross-validated accuracy of 90.79 ± 4.57%, 89.49 ± 5.20%, 90.45 ± 4.24%, and 97.46 ± 3.26%, respectively. The identified genes enriched the gene ontology of transcriptional regulation, inflammatory response, neuron projection, and the cytoskeleton. Moreover, three genes in the cognitive, sensory, and perceptual systems were also identified.Conclusions: The mutant HTT may interfere with both the expression and transport of these identified genes to promote the HD pathogenesis.

Amyotrophic lateral sclerosis (ALS), the most prevalent motor neuron disease characterized by its complex genetic structure, lacks a single diagnostic test capable of providing a conclusive diagnosis. In order to demonstrate the potential for genetic diagnosis and shed light on the pathogenic role of miRNAs in ALS, we developed an ALS diagnostic rule by training the model using 80% of a miRNA profiling dataset consisting of 253 ALS samples and 103 control samples. Subsequently, we validated the diagnostic rule using the remaining 20% of unseen samples. The diagnostic rule we developed includes miR-205-5p, miR-206, miR-376a-5p, miR-412-5p, miR-3927-3p, miR-4701-3p, miR-6763-5p, and miR-6801-3p. Remarkably, the rule achieved an 82% true positive rate and a 73% true negative rate when predicting the unseen samples. Furthermore, the identified miRNAs target 21 genes in the PI3K-Akt pathway and 27 genes in the ALS pathway, including notable genes such as BCL2, NEFH, and OPTN. We propose that miRNA profiling may serve as a complementary diagnostic tool to supplement the clinical presentation and aid in the early recognition of ALS.