Objective: To investigate the inhibitory effect of neoeriocitrin (Neo) on ligature-induced experimental periodontitis in mice and evaluate its biosafety, providing experimental evidence for novel candidate drugs in periodontitis treatment. Methods: This study has been approved by the Animal Welfare and Ethical Safety Committee. A periodontitis model was established in C57BL/6J mice using silk ligation. The mice were divided into control, periodontitis model, and three Neo treatment groups (5, 10, 20 mg/kg Neo). After 2 weeks of intervention, alveolar bone resorption was analyzed by micro-computed tomography (micro-CT); periodontal tissue pathological changes were observed via hematoxylin-eosin (HE) and Masson staining; osteoclasts were counted using tartrate-resistant acid phosphatase staining; serum inflammatory factor levels [tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), interleukin-1β (IL-1β), interleukin-10 (IL-10)] were detected by enzyme-linked immunosorbent assay; gut microbiota changes were analyzed by 16S rRNA sequencing; and biosafety was assessed through HE staining of the heart, liver, spleen, lung, kidney, and colon, as well as zonula occludens-1 (ZO-1) immunohistochemical staining of the colon. Results: Compared with the periodontitis model group, the 10 mg/kg Neo treatment group showed significantly alleviated alveolar bone resorption, manifested as reduced cementoenamel junction to alveolar bone crest distance, increased bone mineral density, bone volume fraction, and trabecular thickness. Additionally, reduced inflammatory cell infiltration, improved collagen fiber arrangement, and a significantly decreased number of osteoclasts were observed in periodontal tissues. Serum levels of pro-inflammatory factors TNF-α and IL-6 were also reduced. Furthermore, 10 mg/kg Neo intervention modulated the gut microbiota structure without causing significant multi-organ toxicity or impairing intestinal barrier function. Conclusion Neo can effectively mitigate the progression of experimental periodontitis by inhibiting alveolar bone resorption, reducing periodontal tissue inflammation, modulating systemic immunity, and improving gut microbiota. With good biosafety, Neo is a promising candidate drug for the treatment of periodontitis.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveTo analyze the processing mechanism underlying the enhanced effect of invigorating spleen and stopping diarrhea of soil-fried Atractylodis Macrocephalae Rhizoma（AMR） by analyzing the changes of microstructure， chemical composition and anti-ulcerative colitis（UC） activity before and after soil stir-frying. MethodsThe microstructure and elemental composition of AMR before and after soil stir-frying were analyzed by scanning electron microscopy-energy dispersive spectroscopy（SEM-EDS）， to investigate the differences in microstructure and the underlying causes. Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS） coupled with UNIFI 1.9.2 natural product analysis platform were used to analyze and identify the chemical constituents in raw and soil-fried products， and multivariate statistical methods including principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to explore the differences and sources of chemical constituents between them. A dextran sulfate sodium（DSS）-induced UC mouse model was established. The method of disease activity index（DAI） was used to evaluate the severity of intestinal inflammation. Hematoxylin-eosin（HE） staining was used to observe the pathological changes of colon tissue， enzyme-linked immunosorbent assay（ELISA） was used to detect the levels of inflammatory factors， Real-time quantitative polymerase chain reaction（Real-time PCR） and Western blot were used to analyze the expressions of key genes and proteins involved in the intestinal mucosal barrier. The 16S rRNA sequencing was used to evaluate the diversity of intestinal flora， headspace gas chromatography-mass spectrometry（HS-GC-MS） was used to explore the levels of short-chain fatty acids（SCFAs） in feces. Base on the above findings， this paper investigated the effects of raw and soil-fried AMR on the biological， chemical， mechanical and immune barriers of model animals， and the differences in pharmacological effects and underlying mechanisms from the perspective of regulating the intestinal mucosal barrier in UC mice. ResultsSEM observation revealed numerous hearth soil particles on the surface of soil-fried AMR， accompanied by bubble-like bulges. At the same time， there were many cracks and folds on the surface of the hearth soil. EDS analysis revealed that the contents of Si， Al， Mg and Ca in soil-fried AMR were significantly higher than those of raw products， and these elements constituted the primary components of hearth soil. UPLC-Q-TOF-MS combined with database comparison was used to identify the chemical constituents of raw and soil-fried AMR. In positive ion mode， a total of 132 components were identified， primarily comprising three categories of terpenoids， polyphenols and amino acids. In negative ion mode， a total of 40 components were identified， primarily polyphenolic and glycoside compounds. Among them， the contents of sesquiterpenes and polyphenolic acids were changed significantly before and after processing. Soil-fried AMR could reduce the DAI score of UC mice， alleviate the shortening of colon length， reduce the levels of pro-inflammatory factors such as interleukin（IL）-17， IL-18， γ-interferon（IFN-γ） and tumor necrosis factor（TNF）-α in serum， increase the levels of anti-inflammatory factors such as secretory immunoglobulin A（sIgA）， IL-10， IL-4 and transforming growth factor-β（TGF-β） in serum， increase the expressions of key genes and proteins of intestinal mucosal barrier such as tight junction protein-1（ZO-1）， Occludin， Claudin-1 and mucin 2（MUC2） in colonic mucosa， and improve the disorders of intestinal flora diversity and the levels of SCFAs（P<0.05， P<0.01）. The raw and stir-fried products of AMR also exhibited the aforementioned effects， but they were weaker than the soil-fried products. Additionally， the auxiliary material hearth soil also had a certain pharmacodynamic effect. ConclusionSoil-fried AMR can enhance the protective effect on intestinal mucosal barrier in UC mice. These changes or heating-induced alterations in the microscopic structure and chemical composition of AMR may be attributed to the dual effects of adsorption of hearth soil.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

AIM:To investigate the effect of ranibizumab on blood flow density in different regions of the macula in patients with macular edema(ME)secondary to ischemic and non-ischemic branch retinal vein occlusion(BRVO).METHODS:This retrospective study enrolled patients with BRVO-ME who were treated at the hospital from September 2019 to March 2021. Patients were divided into ischemic and non-ischemic groups based on fundus findings. All patients received intravitreal injections of ranibizumab once monthly for three consecutive months. Best corrected visual acuity(BCVA), central macular thickness(CMT), and macular blood flow density were measured before treatment and at 1 d, 1 wk, 1 and 3 mo after treatment.RESULTS: A total of 46 patients(46 eyes)with BRVO-ME were included, comprising 21 eyes in the ischemic group(7 males, 14 females; mean age 55.81±10.36 y)and 25 eyes in the non-ischemic group(11 males, 14 females; mean age 54.84±9.81 y). At 3 mo after treatment, BCVA(LogMAR)in the non-ischemic group was superior to that in the ischemic group(0.19±0.19 vs 0.38±0.27, P=0.009). Analysis of CMT changes showed that the reduction amplitude in the ischemic group was significantly greater than that in the non-ischemic group at both 1 and 3 mo after treatment(all P<0.05). Blood flow densities in the whole, parafoveal, and perifoveal regions of the superficial capillary plexus(SCP), as well as in the whole and perifoveal regions of the deep capillary plexus(DCP), were significantly lower in ischemic patients than in non-ischemic patients, while blood flow density in the foveal region of DCP was significantly higher in the ischemic group(all P<0.05).CONCLUSION: Ranibizumab is effective for both types of patients. Non-ischemic patients have a better long-term visual prognosis, and the advantage may be related to better blood flow perfusion patterns in specific areas 3 mo after treatment. Monitoring changes in blood flow density in these areas can help provide personalized treatment for patients.

ObjectiveThis paper aims to find the identified and validated clinical biomarker data building upon a clinical study of early-phase phase Ⅱ and investigate the correlation analysis of Huanglian Jiedu Wan on syndrome improvement and clinical biomarkers in the treatment of "excess heat-toxicity" based on a machine learning model. Additionally， the effective prediction of clinical biomarker values for the main symptoms of the "excess heat-toxicity" syndrome was assessed. MethodsA total of 229 patients meeting the inclusion criteria for "excess heat-toxicity" syndrome were randomly divided into the Huanglian Jiedu Wan group and the placebo group. Syndrome score transition matrices were constructed for the Huanglian Jiedu Wan group and the placebo group based on three main symptoms of "excess heat-toxicity" syndrome， such as oral ulcers， sore throat， and gum swelling and pain. Data from the patients with these three syndromes were also integrated for an overall analysis. The corresponding syndrome score transition matrices were further constructed to visualize symptom change trends of the patients in the two groups via heatmaps. Based on the identified and validated clinical biomarkers related to inflammation， oxidative stress， and energy metabolism in the early phase， Spearman correlation analysis was employed to analyze and evaluate the associations between clinical biomarkers and syndrome improvement. Key clinical biomarkers reflecting the effect of Huanglian Jiedu Wan were screened through the comparison of differences between groups. An extreme gradient boosting （XGBoost） algorithm was used to develop a prediction model for main symptom classification， with classification performance evaluated through 10-fold cross-validation. Feature importance analysis was applied to identify variables with the greatest contribution to the prediction result. ResultsThe syndrome transition matrix results indicated that the Huanglian Jiedu Wan group showed a superior effect to the placebo group in improving oral ulcers， sore throat， and overall symptoms， with significant effects observed especially in sore throat and overall symptom analyses （P<0.01）. Spearman correlation analysis revealed that several clinical biomarkers positively correlated with "excess heat-toxicity" syndrome and its main symptom improvement， were also called "heat-related biomarkers"， including succinic acid， α-ketoglutaric acid， glycine， lactic acid， adenosine monophosphate （AMP）， tumor necrosis factor-α （TNF-α）， interferon-γ （IFN-γ）， interleukin-1β （IL-1β）， interleukin-4 （IL-4）， interleukin-6 （IL-6）， interleukin-8 （IL-8）， interleukin-10 （IL-10）， and so on. Conversely， clinical biomarkers negatively correlated with symptom severity， were also called "heat-clearing related biomarkers" after administration of Huanglian Jiedu Wan， including malic acid， fumaric acid， cis-aconitic acid， adrenocorticotropic hormone （ACTH）， IL-1β， IL-4， IL-8， succinic acid， and citric acid. The XGBoost classification model using all 52 biomarkers as variables achieved an average test accuracy of 0.754 and an average F1 score of 0.777. Feature importance analysis identified the scores of glutamic acid in saliva and IL-6 were the highest in all the variables， with importance scores of 0.081 and 0.080， respectively. After screening out 14 key variables and optimizing the parameters， model performance improved to an average accuracy of 0.758 and an F1 score of 0.798. Feature importance analysis further determined that the glutamic acid in saliva and IL-6 showed obvious changes after screening the variables， confirming the good syndrome prediction ability of the model constructed by these key clinical biomarkers. ConclusionThis study systematically elucidates the correlation between syndrome improvement and clinical biomarkers of Huanglian Jiedu Wan in the treatment of "excess heat-toxicity" syndrome. An XGBoost classification model based on key clinical biomarkers is successfully established， achieving effective prediction of the symptoms related to the "excess heat-toxicity" syndrome such as oral ulcers and sore throat and providing a new insight for objective identification of traditional Chinese medicine syndromes.

ObjectiveTo analyze the processing mechanism underlying the enhanced effect of invigorating spleen and stopping diarrhea of soil-fried Atractylodis Macrocephalae Rhizoma（AMR） by analyzing the changes of microstructure， chemical composition and anti-ulcerative colitis（UC） activity before and after soil stir-frying. MethodsThe microstructure and elemental composition of AMR before and after soil stir-frying were analyzed by scanning electron microscopy-energy dispersive spectroscopy（SEM-EDS）， to investigate the differences in microstructure and the underlying causes. Ultra-high performance liquid chromatography-quadrupole-time-of-flight mass spectrometry（UPLC-Q-TOF-MS） coupled with UNIFI 1.9.2 natural product analysis platform were used to analyze and identify the chemical constituents in raw and soil-fried products， and multivariate statistical methods including principal component analysis（PCA） and orthogonal partial least squares-discriminant analysis（OPLS-DA） were used to explore the differences and sources of chemical constituents between them. A dextran sulfate sodium（DSS）-induced UC mouse model was established. The method of disease activity index（DAI） was used to evaluate the severity of intestinal inflammation. Hematoxylin-eosin（HE） staining was used to observe the pathological changes of colon tissue， enzyme-linked immunosorbent assay（ELISA） was used to detect the levels of inflammatory factors， Real-time quantitative polymerase chain reaction（Real-time PCR） and Western blot were used to analyze the expressions of key genes and proteins involved in the intestinal mucosal barrier. The 16S rRNA sequencing was used to evaluate the diversity of intestinal flora， headspace gas chromatography-mass spectrometry（HS-GC-MS） was used to explore the levels of short-chain fatty acids（SCFAs） in feces. Base on the above findings， this paper investigated the effects of raw and soil-fried AMR on the biological， chemical， mechanical and immune barriers of model animals， and the differences in pharmacological effects and underlying mechanisms from the perspective of regulating the intestinal mucosal barrier in UC mice. ResultsSEM observation revealed numerous hearth soil particles on the surface of soil-fried AMR， accompanied by bubble-like bulges. At the same time， there were many cracks and folds on the surface of the hearth soil. EDS analysis revealed that the contents of Si， Al， Mg and Ca in soil-fried AMR were significantly higher than those of raw products， and these elements constituted the primary components of hearth soil. UPLC-Q-TOF-MS combined with database comparison was used to identify the chemical constituents of raw and soil-fried AMR. In positive ion mode， a total of 132 components were identified， primarily comprising three categories of terpenoids， polyphenols and amino acids. In negative ion mode， a total of 40 components were identified， primarily polyphenolic and glycoside compounds. Among them， the contents of sesquiterpenes and polyphenolic acids were changed significantly before and after processing. Soil-fried AMR could reduce the DAI score of UC mice， alleviate the shortening of colon length， reduce the levels of pro-inflammatory factors such as interleukin（IL）-17， IL-18， γ-interferon（IFN-γ） and tumor necrosis factor（TNF）-α in serum， increase the levels of anti-inflammatory factors such as secretory immunoglobulin A（sIgA）， IL-10， IL-4 and transforming growth factor-β（TGF-β） in serum， increase the expressions of key genes and proteins of intestinal mucosal barrier such as tight junction protein-1（ZO-1）， Occludin， Claudin-1 and mucin 2（MUC2） in colonic mucosa， and improve the disorders of intestinal flora diversity and the levels of SCFAs（P<0.05， P<0.01）. The raw and stir-fried products of AMR also exhibited the aforementioned effects， but they were weaker than the soil-fried products. Additionally， the auxiliary material hearth soil also had a certain pharmacodynamic effect. ConclusionSoil-fried AMR can enhance the protective effect on intestinal mucosal barrier in UC mice. These changes or heating-induced alterations in the microscopic structure and chemical composition of AMR may be attributed to the dual effects of adsorption of hearth soil.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

Objective To construct glucose oxidase（GOx）–loaded nanogels （GONGs）, optimize their formulation, and evaluate their capacity to inhibit the Warburg effect in glioma cells. Methods A responsive polymer （HAM） was synthesized and used to self-assemble GONGs, which were then characterized. Encapsulation efficiency and drug loading were determined using fluorescence spectrophotometry. Biocompatibility was tested by measuring cytotoxicity and hemolytic activity. Western blotting was used to evaluate the effects of GONGs on the expression of proteins associated with the Warburg phenotype and oxidative damage in glioma cells. Results GONGs prepared at a drug–to–polymer ratio of 1∶10 exhibited a particle size of 140.3 nm and a zeta potential of −27.2 mV. Compared with free GOx, GONGs markedly reduced cytotoxicity, increased the IC₅₀ in hUVEC cells from 2.150 nmol/L to 74.86 nmol/L, and significantly decreased hemolysis. At a GOx concentration of 2 nmol/L, GONGs effectively downregulated glycolysis-related proteins, such as HK2 and LDHA, and inhibited glutamine metabolism in glioma cells. Conclusion GONGs exhibited high GOx loading capacity, significantly reduced GOx-induced cytotoxicity, inhibited the Warburg effect in glioma cells and induced oxidative damage.

Work-related musculoskeletal disorders (WMSDs) represent a significant occupational health challenge among healthcare professionals globally, posing substantial threats to physical and mental well-being as well as work sustainability. Adopting preventive behaviors—including ergonomic postural adjustments, optimized work-rest scheduling, proper use of protective and assistive equipment, and regular physical activity—is essential for mitigating the risk of WMSDs. Guided by the social ecological model, the review synthesized current evidence on the determinants of WMSDs preventive behaviors across four levels: intrapersonal characteristics, work environment conditions, interpersonal support, and policy/institutional factors. The findings suggest that higher educational attainment, favorable health-related behavioral patterns, optimized ergonomic work environments, adoption of supportive collaborative systems, strong organizational support, as well as policy safeguards facilitate preventive behavior adoption. Conversely, limited prevention-related knowledge, low risk perception, insufficient physical activity, excessive workload, lack of appropriate protective equipment, inadequate ergonomic training, a prevailing culture of presenteeism, and inadequate policy implementation constitute significant barriers. Multi-dimensional intervention strategies targeting these determinants are warranted to enhance preventive behaviors, reduce the risk of WMSDs, and strengthen occupational health protection for healthcare professionals.