Pharmacoacupuncture for the Treatment of Frozen Shoulder: A Systematic Review and Meta-Analysis of Randomized Controlled Trials.

BACKGROUND: Pharmacoacupuncture (PA) is an alternative injection therapy for a broad range of conditions. This meta-analysis evaluates the effectiveness and safety of PA in treating frozen shoulder (FS) and aims to standardise PA characteristics in clinical practice. METHODS: Randomized controlled trials (RCTs) assessing PA for FS were systematically reviewed from seven electronic databases up to August 31, 2023. Outcomes measured included the visual analogue scale (VAS) or numerical rating scale (NRS), effective rate, Constant-Murley Score (CMS), Shoulder Pain and Disability Index (SPADI), ROM, quality of life (QoL), and adverse events. Data analysis was conducted using RevMan 5.3, with the risk of bias in each trial evaluated using Cochrane's risk of bias tool. The Grading of Recommendations Assessment, Development, and Evaluation (GRADE) tool assessed the quality of evidence. RESULTS: Thirteen RCTs involving 1034 patients met the inclusion criteria, with eleven RCTs and 872 patients included in the meta-analysis. PA showed low-quality evidence of improvement in VAS, effective rate, CMS Total, and CMS Pain. Moderate-quality evidence indicated improvement in CMS ADL and CMS Mobility. PA was effective in four SF-36 subscales: physical function, social role function, mental health, and emotional role function. No significant difference in CMS strength was observed. One trial reported mild anaphylaxis reaction to bee venom as an adverse event. CONCLUSION: PA may offer potential benefits as an alternative injection therapy for FS patients. Further well-designed RCTs with rigorous methodology are required to substantiate its therapeutic efficacy and clinical utility. REGISTRATION: PROSPERO (CRD42023445708).

The Effectiveness and Safety of Yi Shen Juan Bi Pill on Rheumatoid Arthritis: a Protocol for Systematic Review and/or Meta-Analysis.

Importance: The Yi Shen Juan Bi (YSJB) pill has been used to treat rheumatoid arthritis (RA), with several studies reporting its anti-inflammatory effects and ability to prevent bone destruction. However, the lack of evidence regarding the effectiveness and toxicity of individual components of YSJB limits its widespread use. Objective: This protocol outlines a systematic review and/or meta-analysis to evaluate the effectiveness and safety of YSJB. Data Resources: We will search worldwide electronic databases, including MEDLINE, Cochrane Library, China National Knowledge Infrastructure, Wanfang, CiNii, J-STAGE, KoreaMed, Korean Medical Database, Korean Studies Information Service System, National Digital Science Library, Korea Institute of Science and Technology Information, and Oriental Medicine Advanced Searching Integrated System. In addition, we will conduct manual searches, and, if necessary, contact authors directly. The search will cover publications until June 2024. Study Selection: We will select randomized controlled trials (RCTs) that compare the use of YSJB for the treatment of RA against other treatments. Study Extraction and Synthesis: Data from the selected RCTs will be extracted, including sample size, patient characteristics, intervention details, and outcome measures. We will perform a meta-analysis using Review Manager software. Main Outcome(s) and Measure(s): The primary outcome measures will include disease activity scores such as effective rate, swollen joint count, tender joint count, and morning stiffness. Secondary outcome measures will include blood test results and adverse events. Results: The results will reveal the effectiveness and safety of YSJB for the treatment of RA. Conclusions: and Relevance: The findings will provide an evidence-based review of the use of YSJB for RA.S.

Systemic Symptoms as Potential Predictors of Chronic Neck Pain on Initial Examination: Can Systemic Symptoms Act as a Predictor of Neck Pain?

BACKGROUND: Neck pain is a prevalent musculoskeletal disorder that can cause other additional misalignments and other misalignment-induced chronic musculoskeletal diseases. Although numerous risk factors for chronic neck pain have been researched, systemic symptoms have not received the same level of investigation. The aim of this study was to analyze the link between subjective systemic symptoms and neck pain based on initial holistic interviews, with the objective of identifying potential predictive factors for neck pain. METHODS: This retrospective cross-sectional study included patients hospitalized due to acute neck pain between January 2018 and August 2021. Data collected included demographic information, treatment details, neck-pain characteristics, medical history, and co-occurring symptoms regardless of their known association with neck pain. Statistical analyses, including independent t-tests, Mann-Whitney U tests, chi-squared tests, Fisher's exact tests, and correlation analyses, were performed. RESULTS: With regard to the demographic characteristics, a significant positive correlation was observed between age and initial pain intensity (p < 0.01), while female sex was associated with changes in pain intensity (p < 0.05). Past medical conditions, including diabetes, hyperlipidemia, heart attacks, and psychological medical history, demonstrated a significant relationship with neck pain (p < 0.01, p < 0.05, p < 0.05, and p < 0.05, respectively). Hospitalization duration, cervical dizziness, limitations in the cervical range of motion (ROM), and widespread pain were significantly associated with neck pain (p < 0.05, p < 0.05, p < 0.01, and p < 0.001, respectively). Among the subjective systemic symptoms, only upper gastrointestinal (GI) disturbance displayed a significant association with neck pain (p < 0.01). CONCLUSIONS: This study identified several potential predictors of neck pain-notably, upper GI disturbances-providing a new avenue to investigate the prognostic factors of neck pain. However, further study is needed to substantiate these findings and elucidate the precise nature of these associations.

Time series forecasting of weight for diuretic dose adjustment using bidirectional long short-term memory.

Loop diuretics are prevailing drugs to manage fluid overload in heart failure. However, adjusting to loop diuretic doses is strenuous due to the lack of a diuretic guideline. Accordingly, we developed a novel clinician decision support system for adjusting loop diuretics dosage with a Long Short-Term Memory (LSTM) algorithm using time-series EMRs. Weight measurements were used as the target to estimate fluid loss during diuretic therapy. We designed the TSFD-LSTM, a bi-directional LSTM model with an attention mechanism, to forecast weight change 48 h after heart failure patients were injected with loop diuretics. The model utilized 65 variables, including disease conditions, concurrent medications, laboratory results, vital signs, and physical measurements from EMRs. The framework processed four sequences simultaneously as inputs. An ablation study on attention mechanisms and a comparison with the transformer model as a baseline were conducted. The TSFD-LSTM outperformed the other models, achieving 85% predictive accuracy with MAE and MSE values of 0.56 and 1.45, respectively. Thus, the TSFD-LSTM model can aid in personalized loop diuretic treatment and prevent adverse drug events, contributing to improved healthcare efficacy for heart failure patients.

Pharmacoacupuncture for the Treatment of Frozen Shoulder: protocol for a systematic review and meta-analysis.

Objectives: Frozen shoulder (FS) is one of the most challenging shoulder disorders for patients and clinicians. Its symptoms mainly include any combination of stiffness, nocturnal pain, and limitation of active and passive glenohumeral joint movement. Conventional treatment options for FS are physical therapy, nonsteroidal anti-inflammatory drugs, injection therapy, and arthroscopic capsular release, but adverse and limited effects continue to present problems. As a result, pharmacoacupuncture (PA) is getting attention as an alternative therapy for patients with FS. PA is a new form of acupuncture treatment in traditional Korean medicine (TKM) that is mainly used for musculoskeletal diseases. It has similarity and specificity compared to corticosteroid injection and hydrodilatation, making it a potential alternative injection therapy for FS. However, no systematic reviews investigating the utilization of PA for FS have been published. Therefore, this review aims to standardize the clinical use of PA for FS and validate its therapeutic effect. Methods: The protocol was registered in Prospero (CRD42023445708) on 18 July 2023. Until Aug. 31, 2023, seven electronic databases will be searched for randomized controlled trials of PA for FS. Authors will be contacted, and manual searches will also be performed. Two reviewers will independently screen and collect data from retrieved articles according to predefined criteria. The primary outcome will be pain intensity, and secondary outcomes will be effective rate, Constant-Murley Score, Shoulder Pain and Disability Index, range of motion, quality of life, and adverse events. Bias and quality of the included trials will be assessed using the Cochrane handbook's risk-of-bias tool for randomized trials. Meta analyses will be conducted using Review Manager V.5.3 software. GRADE will be used to evaluate the level of evidence for each outcome. Results: This systematic review and meta-analysis will be conducted following PRISMA statement. The results will be published in a peer-reviewed journal. Conclusion: This review will provide scientific evidence to support health insurance policy as well as the standardization of PA in clinical practice.

Forecasting Hospital Room and Ward Occupancy Using Static and Dynamic Information Concurrently: Retrospective Single-Center Cohort Study.

BACKGROUND: Predicting the bed occupancy rate (BOR) is essential for efficient hospital resource management, long-term budget planning, and patient care planning. Although macro-level BOR prediction for the entire hospital is crucial, predicting occupancy at a detailed level, such as specific wards and rooms, is more practical and useful for hospital scheduling. OBJECTIVE: The aim of this study was to develop a web-based support tool that allows hospital administrators to grasp the BOR for each ward and room according to different time periods. METHODS: We trained time-series models based on long short-term memory (LSTM) using individual bed data aggregated hourly each day to predict the BOR for each ward and room in the hospital. Ward training involved 2 models with 7- and 30-day time windows, and room training involved models with 3- and 7-day time windows for shorter-term planning. To further improve prediction performance, we added 2 models trained by concatenating dynamic data with static data representing room-specific details. RESULTS: We confirmed the results of a total of 12 models using bidirectional long short-term memory (Bi-LSTM) and LSTM, and the model based on Bi-LSTM showed better performance. The ward-level prediction model had a mean absolute error (MAE) of 0.067, mean square error (MSE) of 0.009, root mean square error (RMSE) of 0.094, and R2 score of 0.544. Among the room-level prediction models, the model that combined static data exhibited superior performance, with a MAE of 0.129, MSE of 0.050, RMSE of 0.227, and R2 score of 0.600. Model results can be displayed on an electronic dashboard for easy access via the web. CONCLUSIONS: We have proposed predictive BOR models for individual wards and rooms that demonstrate high performance. The results can be visualized through a web-based dashboard, aiding hospital administrators in bed operation planning. This contributes to resource optimization and the reduction of hospital resource use.

Are polypharmacy side effects predicted by public data still valid in real-world data?

Background and Objective: Although interest in predicting drug-drug interactions is growing, many predictions are not verified by real-world data. This study aimed to confirm whether predicted polypharmacy side effects using public data also occur in data from actual patients. Methods: We utilized a deep learning-based polypharmacy side effects prediction model to identify cefpodoxime-chlorpheniramine-lung edema combination with a high prediction score and a significant patient population. The retrospective study analyzed patients over 18 years old who were admitted to the Asan medical center between January 2000 and December 2020 and took cefpodoxime or chlorpheniramine orally. The three groups, cefpodoxime-treated, chlorpheniramine-treated, and cefpodoxime & chlorpheniramine-treated were compared using inverse probability of treatment weighting (IPTW) to balance them. Differences between the three groups were analyzed using the Kaplan-Meier method and Cox proportional hazards model. Results: The study population comprised 54,043 patients with a history of taking cefpodoxime, 203,897 patients with a history of taking chlorpheniramine, and 1,628 patients with a history of taking cefpodoxime and chlorpheniramine simultaneously. After adjustment, the 1-year cumulative incidence of lung edema in the patient group that took cefpodoxime and chlorpheniramine simultaneously was significantly higher than in the patient groups that took cefpodoxime or chlorpheniramine only (p=0.001). Patients taking cefpodoxime and chlorpheniramine together had an increased risk of lung edema compared to those taking cefpodoxime alone [hazard ratio (HR) 2.10, 95% CI 1.26-3.52, p<0.005] and those taking chlorpheniramine alone, which also increased the risk of lung edema (HR 1.64, 95% CI 0.99-2.69, p=0.05). Conclusions: Validation of polypharmacy side effect predictions with real-world data can aid patient and clinician decision-making before conducting randomized controlled trials. Simultaneous use of cefpodoxime and chlorpheniramine was associated with a higher long-term risk of lung edema compared to the use of cefpodoxime or chlorpheniramine alone.

LDP-GAN : Generative adversarial networks with local differential privacy for patient medical records synthesis.

Electronic medical records(EMR) have considerable potential to advance healthcare technologies, including medical AI. Nevertheless, due to the privacy issues associated with the sharing of patient's personal information, it is difficult to sufficiently utilize them. Generative models based on deep learning can solve this problem by creating synthetic data similar to real patient data. However, the data used for training these deep learning models run into the risk of getting leaked because of malicious attacks. This means that traditional deep learning-based generative models cannot completely solve the privacy issues. Therefore, we suggested a method to prevent the leakage of training data by protecting the model from malicious attacks using local differential privacy(LDP). Our method was evaluated in terms of utility and privacy. Experimental results demonstrated that the proposed method can generate medical data with reasonable performance while protecting training data from malicious attacks.

Prediction of hospitalization and waiting time within 24 hours of emergency department patients with unstructured text data.

Overcrowding of emergency departments is a global concern, leading to numerous negative consequences. This study aimed to develop a useful and inexpensive tool derived from electronic medical records that supports clinical decision-making and can be easily utilized by emergency department physicians. We presented machine learning models that predicted the likelihood of hospitalizations within 24 hours and estimated waiting times. Moreover, we revealed the enhanced performance of these machine learning models compared to existing models by incorporating unstructured text data. Among several evaluated models, the extreme gradient boosting model that incorporated text data yielded the best performance. This model achieved an area under the receiver operating characteristic curve score of 0.922 and an area under the precision-recall curve score of 0.687. The mean absolute error revealed a difference of approximately 3 hours. Using this model, we classified the probability of patients not being admitted within 24 hours as Low, Medium, or High and identified important variables influencing this classification through explainable artificial intelligence. The model results are readily displayed on an electronic dashboard to support the decision-making of emergency department physicians and alleviate overcrowding, thereby resulting in socioeconomic benefits for medical facilities.

Machine learning models to predict the warfarin discharge dosage using clinical information of inpatients from South Korea.

As warfarin has a narrow therapeutic window and obvious response variability among individuals, it is difficult to rapidly determine personalized warfarin dosage. Adverse drug events(ADE) resulting from warfarin overdose can be critical, so that typically physicians adjust the warfarin dosage through the INR monitoring twice a week when starting warfarin. Our study aimed to develop machine learning (ML) models that predicts the discharge dosage of warfarin as the initial warfarin dosage using clinical data derived from electronic medical records within 2 days of hospitalization. During this retrospective study, adult patients who were prescribed warfarin at Asan Medical Center (AMC) between January 1, 2018, and October 31, 2020, were recruited as a model development cohort (n = 3168). Additionally, we created an external validation dataset (n = 891) from a Medical Information Mart for Intensive Care III (MIMIC-III). Variables for a model prediction were selected based on the clinical rationale that turned out to be associated with warfarin dosage, such as bleeding. The discharge dosage of warfarin was used the study outcome, because we assumed that patients achieved target INR at discharge. In this study, four ML models that predicted the warfarin discharge dosage were developed. We evaluated the model performance using the mean absolute error (MAE) and prediction accuracy. Finally, we compared the accuracy of the predictions of our models and the predictions of physicians for 40 data point to verify a clinical relevance of the models. The MAEs obtained using the internal validation set were as follows: XGBoost, 0.9; artificial neural network, 0.9; random forest, 1.0; linear regression, 1.0; and physicians, 1.3. As a result, our models had better prediction accuracy than the physicians, who have difficulty determining the warfarin discharge dosage using clinical information obtained within 2 days of hospitalization. We not only conducted the internal validation but also external validation. In conclusion, our ML model could help physicians predict the warfarin discharge dosage as the initial warfarin dosage from Korean population. However, conducting a successfully external validation in a further work is required for the application of the models.

In Silico Identification and In Vitro Validation of Repurposed Compounds Targeting the RSV Polymerase.

Respiratory Syncytial Virus (RSV) is the top cause of infant hospitalization globally, with no effective treatments available. Researchers have sought small molecules to target the RNA-dependent RNA Polymerase (RdRP) of RSV, which is essential for replication and transcription. Based on the cryo-EM structure of the RSV polymerase, in silico computational analysis including molecular docking and the protein-ligand simulation of a database, including 6554 molecules, is currently undergoing phases 1-4 of clinical trials and has resulted in the top ten repurposed compound candidates against the RSV polymerase, including Micafungin, Totrombopag, and Verubecestat. We performed the same procedure to evaluate 18 small molecules from previous studies and chose the top four compounds for comparison. Among the top identified repurposed compounds, Micafungin, an antifungal medication, showed significant inhibition and binding affinity improvements over current inhibitors such as ALS-8112 and Ribavirin. We also validated Micafungin's inhibition of the RSV RdRP using an in vitro transcription assay. These findings contribute to RSV drug development and hold promise for broad-spectrum antivirals targeting the non-segmented negative-sense (NNS) RNA viral polymerases, including those of rabies (RABV) and Ebola (EBOV).

Direct and Indirect Evidence of Effects of Bacteroides spp. on Obesity and Inflammation.

Metabolic disorders present a significant public health challenge globally. The intricate relationship between the gut microbiome, particularly Bacteroides spp. (BAC), and obesity, including their specific metabolic functions, remains partly unresolved. This review consolidates current research on BAC's role in obesity and lipid metabolism, with three objectives: (1) To summarize the gut microbiota's impact on obesity; (2) To assess BAC's efficacy in obesity intervention; (3) To explore BAC's mechanisms in obesity and lipid metabolism management. This review critically examines the role of BAC in obesity, integrating findings from clinical and preclinical studies. We highlight the changes in BAC diversity and concentration following successful obesity treatment and discuss the notable differences in BAC characteristics among individuals with varying obesity levels. Furthermore, we review recent preclinical studies demonstrating the potential of BAC in ameliorating obesity and related inflammatory conditions, providing detailed insights into the methodologies of these in vivo experiments. Additionally, certain BAC-derived metabolites have been shown to be involved in the regulation of host lipid metabolism-related pathways. The enhanced TNF production by dendritic cells following BAC administration, in response to LPS, also positions BAC as a potential adjunctive therapy in obesity management.

Efficacy and safety of anti-obesity herbal medicine focused on pattern identification: A systematic review and meta-analysis.

BACKGROUND: Herbal medicine based on pattern identification (PI) is used widely in Traditional Chinese Medicine. Proper herbal medicine based on PI has been suggested for effective weight reduction and decreasing the adverse events. This systematic review examined the effectiveness and safety of herbal medicine, focusing on PI in treating obesity. METHODS: Eight electric databases were used for searching randomized controlled trials (RCT) (to August 31, 2021). RCTs which prescribed herbal medicine to obese patients based on PI were included. Body weight (BW) and body mass index (BMI) were the primary outcomes. The risk of bias was assessed using Cochrane risk of bias tool, and the meta-analysis was conducted. Grading the evidence was conducted by using GRADEpro. RESULTS: Sixteen RCTs (1052 patients) were included: 2 studies compared herbal medicine to placebo (128 patients); 2 studies compared them to western medication (161 patients); 12 studies compared them with usual care (763 patients). The meta-analysis showed that the herbal formulas reduced the BW and BMI without significant Adverse events compared to the control group (BW: mean difference  = -4.10, 95% confidence interval: -5.14 to -3.06, I2 = 2% and BMI: mean difference = -1.53, 95% confidence interval: -1.88 to -1.19, I2 = 25%). Moderate-quality evidence on the primary outcomes was found. CONCLUSIONS: Herbal medicine - has good clinical efficacy and safety in treating obesity. This study has limitations that some literatures with high risk of bias in blinding or without using a standardized diagnosis of PI were included. However, the current evidence suggests the possibility of precision medicine using PI.

A survey of the clinical practice followed by Korean medicine doctors for scoliosis: Preliminary data for clinical practice guidelines.

Scoliosis is a spinal disease in which the Cobb angle is >10°. Scoliosis treatment can be surgical or conservative, and clinical practice guidelines (CPGs) for conservative treatments have been updated since 2006. There have been several articles regarding the efficacy and safety of Korean medicine (KM) in treating scoliosis, but there are no CPGs. Our study investigated the current clinical practice using a survey for the future establishment of KM-CPGs. The survey mainly comprised clinical practice status, diagnosis, treatment, progress and prognosis, and perception of KM on scoliosis, with reference to existing surveys of other musculoskeletal disorders and scoliosis-related articles. A web-based survey was conducted from February 16, 2021 to February 28, 2021. We found that 60% of KM doctors (KMDs) respondents treated patients with scoliosis, and they valued radiographical measurements and scoliosis-related factors in the diagnosis. KMDs used multiple KM treatments, including acupuncture, Chuna, cupping, pharmacopuncture, and herbal medicine, and they emphasized the importance of KM more in nonstructural scoliosis than in structural scoliosis. Although the perception of the prognosis of scoliosis was in agreement with that suggested by previous guidelines, KMDs showed outstanding focus on reduction of symptoms of scoliosis and improvement of quality of life. Despite some limitations, including low participation and the need for consulting other medical practitioners, our study may be helpful for the development of KM-CPGs because it is the first to analyze the perceptions of KMDs on scoliosis and to collect preliminary data that are of significance for preparing clinical guidelines.

The Positive Effects of Poria cocos Extract on Quality of Sleep in Insomnia Rat Models.

Sleep disorders may have various causes and can incur mental and/or physical symptoms, and affect an individual's quality of life. In this study, we confirm that the Poria cocos extract (PCET) can improve sleep quality and structure by promoting inhibitory neurotransmission via the γ-aminobutyric acid (GABA) type A (GABAA) receptors based on the mechanisms revealed in the experiment with superior cervical ganglion neurons. Pentobarbital-induced sleep tests were conducted in order to determine whether the PCET extract improves the sleep quality and structure in normal ICR mice. Sleep latency and duration were checked with the righting reflex. To simulate the state of awakening as well as a normal sleep state, caffeine was administered orally before the PCET diet. After oral gavage of PCET, sleep latency was decreased, and total sleep duration was increased in normal and caffeine-induced sleep disturbance state. In the ACTH-induced sleep disturbed models, administration of PCET significantly reduced the sleep latency and increased the non-REM sleep duration, which was analyzed in real-time EEG by implanting wireless electrodes in SD rats. PCET was found to improve the sleep quality under a normal sleep state through the GABAA receptor; it also promoted and improved the sleep quality and sleep structure in both the arousal activation state and stress-based sleep disturbance.

Lowering n-6/n-3 Ratio as an Important Dietary Intervention to Prevent LPS-Inducible Dyslipidemia and Hepatic Abnormalities in ob/ob Mice.

Obesity is closely associated with low-grade chronic and systemic inflammation and dyslipidemia, and the consumption of omega-3 polyunsaturated fatty acids (n-3 PUFAs) may modulate obesity-related disorders, such as inflammation and dyslipidemia. An emerging research question is to understand the dietary intervention strategy that is more important regarding n-3 PUFA consumption: (1) a lower ratio of n-6/n-3 PUFAs or (2) a higher amount of n-3 PUFAs consumption. To understand the desirable dietary intervention method of n-3 PUFAs consumption, we replaced lard from the experimental diets with either perilla oil (PO) or corn oil (CO) to have identical n-3 amounts in the experimental diets. PO had a lower n-6/n-3 ratio, whereas CO contained higher amounts of PUFAs; it inherently contained relatively lower n-3 but higher n-6 PUFAs than PO. After the 12-week dietary intervention in ob/ob mice, dyslipidemia was observed in the normal chow and CO-fed ob/ob mice; however, PO feeding increased the high density lipoprotein-cholesterol (HDL-C) level; further, not only did the HDL-C level increase, the low density lipoprotein-cholesterol (LDL-C) and triglyceride (TG) levels also decreased significantly after lipopolysaccharide (LPS) injection. Consequently, extra TG accumulated in the liver and white adipose tissue (WAT) of normal chow- or CO-fed ob/ob mice after LPS injection; however, PO consumption decreased serum TG accumulation in the liver and WAT. PUFAs replacement attenuated systemic inflammation induced by LPS injection by increasing anti-inflammatory cytokines but inhibiting pro-inflammatory cytokine production in the serum and WAT. PO further decreased hepatic inflammation and fibrosis in comparison with the ND and CO. Hepatic functional biomarkers (aspartate aminotransferase (AST) and alanine transaminase (ALT) levels) were also remarkably decreased in the PO group. In LPS-challenged ob/ob mice, PO and CO decreased adipocyte size and adipokine secretion, with a reduction in phosphorylation of MAPKs compared to the ND group. In addition, LPS-inducible endoplasmic reticulum (ER) and oxidative stress decreased with consumption of PUFAs. Taken together, PUFAs from PO and CO play a role in regulating obesity-related disorders. Moreover, PO, which possesses a lower ratio of n-6/n-3 PUFAs, remarkably alleviated metabolic dysfunction in LPS-induced ob/ob mice. Therefore, an interventional trial considering the ratio of n-6/n-3 PUFAs may be desirable for modulating metabolic complications, such as inflammatory responses and ER stress in the circulation, liver, and/or WAT.

Partial Replacement of High-Fat Diet with Beef Tallow Attenuates Dyslipidemia and Endoplasmic Reticulum Stress in db/db Mice.

High-fat diet (HFD) consumption is closely associated with an increased risk of metabolic syndromes (MetS), such as obesity, type 2 diabetes, and cardiovascular diseases (CVDs). Therefore, the consumption of alternative and functional fatty acids to replace saturated fatty acids and/or trans-fatty acids with polyunsaturated fatty acids has become an important dietary strategy for the prevention of MetS. Consumption of omega-3 fatty acids (n-3) reduces various physiological complications, including CVDs, nonalcoholic fatty liver disease, and insulin resistance, related to inflammatory responses. In this study, we investigated the partial replacement effects of HFD with beef tallow (BT) on dyslipidemia and endoplasmic reticulum (ER) stress in male db/db mice. The animals were grouped to one of four dietary intervention groups (n = 16 per group): (1) normal diet, (2) HFD, (3) HFD partially replaced with regular beef tallow (HFD+BT1), or (4) HFD partially replaced with beef tallow containing a relatively reduced omega-6 fatty acid (n-6)/n-3 ratio (HFD+BT2) than HFD+BT1. After 6 weeks of dietary intervention, 1 mg/kg of phosphate-buffered saline or tunicamycin (TM) was injected intraperitoneally. HFD+BT2 significantly suppressed the serum total cholesterol and non-high-density lipoprotein cholesterol levels more than HFD and HFD+BT1, and triglyceride levels in the epididymal adipose tissue (EAT) were remarkably decreased. Mice that received HFD+BT2 had elevated protein expressions of phospho-AMP-activated protein kinase (p-AMPK). Moreover, HFD+BT2 effectively inhibited ER stress in the liver and EAT. Consistent with our hypothesis, HFD+BT2 remarkably alleviated dyslipidemia and TM-inducible ER stress, while activating p-AMPK.

A Clinical Study on the Relationship Among Insomnia, Tongue Diagnosis, and Oral Microbiome.

Currently, there is a lack of adequate methods to assess insomnia objectively. This study addresses the usefulness of tongue features and oral microbial profile as a potential diagnostic biomarker of insomnia. One hundred insomniac patients and 20 healthy control subjects were selected. Their demographic and clinical characteristics, as well as the tongue diagnostic indices and oral microbial profile, were examined. Compared to the control group, insomniac patients showed a higher abnormal low-frequency/high-frequency (LF/HF) ratio. In tongue diagnosis, the indices related to lightness of tongue body and tongue coating were higher in the insomniac group vs. the control group. Furthermore, linear discriminant analysis (LDA) of oral microbial population revealed that the relative abundances of Clostridia, Veillonella, Bacillus and Lachnospiraceae were significantly higher in the insomniac patients than the control group. Additionally, the tongue features of the insomniac group exhibited that the non-coating group had a poor sleep condition compared to the thick-coating group, although the difference was insignificant. On the other hand, the oral microbial communities of the insomniac patients revealed greater alpha and beta diversities in the non-coating group vs. the thick-coating group. The alpha and beta diversities were higher in orotype1 than orotype2. Collectively, this study highlighted that the lightness of tongue body and tongue coating as well as oral microbial profiles of SR1, Actinobacteria, Clostridia and Lachnospiraceae_unclassified could be considered potential biomarkers of insomnia.

Partial Replacement of Dietary Fat with Krill Oil or Coconut Oil Alleviates Dyslipidemia by Partly Modulating Lipid Metabolism in Lipopolysaccharide-Injected Rats on a High-Fat Diet.

This study investigated the effects of partial replacement of dietary fat with krill oil (KO) or coconut oil (CO) on dyslipidemia and lipid metabolism in rats fed with a high-fat diet (HFD). Sprague Dawley rats were divided into three groups as follows: HFD, HFD + KO, and HFD + CO. The rats were fed each diet for 10 weeks and then intraperitoneally injected with phosphate-buffered saline (PBS) or lipopolysaccharide (LPS) (1 mg/kg). The KO- and CO-fed rats exhibited lower levels of serum lipids and aspartate aminotransferases than those of the HFD-fed rats. Rats fed with HFD + KO displayed significantly lower hepatic histological scores and hepatic triglyceride (TG) content than rats fed with HFD. The KO supplementation also downregulated the adipogenic gene expression in the liver. When treated with LPS, the HFD + KO and HFD + CO groups reduced the adipocyte size in the epididymal white adipose tissues (EAT) relative to the HFD group. These results suggest that KO and CO could improve lipid metabolism dysfunction.