Currently, the drug delivery system (DDS) based on nanomaterials has become a hot interdisciplinary research topic. One of the core issues is drug loading and controlled release, in which the key lever is carriers. Vaterite, as an inorganic porous nano-material, is one metastable structure of calcium carbonate, full of micro or nano porous. Recently, vaterite has attracted more and more attention, due to its significant advantages, such as rich resources, easy preparations, low cost, simple loading procedures, good biocompatibility and many other good points. Vaterite, gained from suitable preparation strategies, can not only possess the good drug carrying performance, like high loading capacity and stable loading efficiency, but also improve the drug release ability, showing the better drug delivery effects, such as targeting release, pH sensitive release, photothermal controlled release, magnetic assistant release, optothermal controlled release. At the same time, the vaterite carriers, with good safety itself, can protect proteins, enzymes, or other drugs from degradation or inactivation, help imaging or visualization with loading fluorescent drugs in vitro and in vivo, and play synergistic effects with other therapy approaches, like photodynamic therapy, sonodynamic therapy, and thermochemotherapy. Latterly, some renewed reports in drug loading and controlled release have led to their widespread applications in diverse fields, from cell level to clinical studies. This review introduces the basic characteristics of vaterite and briefly summarizes its research history, followed by synthesis strategies. We subsequently highlight recent developments in drug loading and controlled release, with an emphasis on the advantages, quantity capacity, and comparations. Furthermore, new opportunities for using vaterite in cell level and animal level are detailed. Finally, the possible problems and development trends are discussed.

In recent years, cancer treatment methods and means are becoming more and more diversified, and single treatment methods often have limited efficacy, while the synergistic effect of immunity combined with chemotherapy can inhibit tumor growth more effectively. Based on this, we constructed a sodium alginate hydrogel composite system loaded with chemotherapeutic agents and tumor vaccines (named SA-DOX-NA) with a view to the combined use of chemotherapeutic agents and tumor vaccines. Firstly, the tumor vaccine (named NA) degradable under acidic conditions was constructed by in situ polymerization using chicken ovalbumin (OVA), acrylamide (AAM) and 2-(dimethylamino) ethyl methacrylate (DMAEMA) monomer. Then a hydrogel composite system SA-DOX-NA co-loaded with chemotherapeutic drug doxorubicin (DOX) and NA was prepared using sodium alginate as a matrix. The results showed that SA-DOX-NA had good in situ gel-forming ability and formed a mesh structure that could realize the co-loading of DOX and NA as well as the slow drug release. The electron microscopy results showed that SA-DOX-NA had good in situ gel-forming ability with good internal connectivity, and the three-dimensional mesh structure could realize the co-loading of DOX and NA as well as the slow drug release. The antitumor and immunomodulatory results showed that SA-DOX-NA both effectively inhibited the growth of tumor cells and efficiently promoted the proliferation and activation of DC2.4 dendritic cells without additional adjuvant. In summary, SA-DOX-NA exerts the dual efficacy of chemotherapy and immunotherapy for tumor treatment, and has a good application prospect in local tumor treatment.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

Metabolites produced as intermediaries or end-products of microbial metabolism provide crucial signals for health and diseases, such as metabolic dysfunction-associated steatotic liver disease (MASLD). These metabolites include products of the bacterial metabolism of dietary substrates, modification of host molecules (such as bile acids [BAs], trimethylamine-N-oxide, and short-chain fatty acids), or products directly derived from bacteria. Recent studies have provided new insights into the association between MASLD and the risk of developing chronic kidney disease (CKD). Furthermore, alterations in microbiota composition and metabolite profiles, notably altered BAs, have been described in studies investigating the association between MASLD and the risk of CKD. This narrative review discusses alterations of specific classes of metabolites, BAs, fructose, vitamin D, and microbiota composition that may be implicated in the link between MASLD and CKD.

OBJECTIVE: To explore the effectiveness and feasibility of two osteotomy guides in medial open wedge high tibial osteotomy (MOWHTO). METHODS: Clinical data of 103 patients who underwent routine MOWHTO surgery between January 2020 and December 2022 were collected for retrospective analysis. The patients were divided into two groups based on the method of osteotomy guide plate. The control group of 51 patients received traditional osteotomy guide plate technique, including 17 males and 34 females, aged from 48 to 68 years old with an average of(57.93±4.82) years old, with a disease duration ranged from 1 to 8 years with an average of (4.89±1.49) years. The observation group of 52 patients received personalized osteotomy guide plate technique, including 23 males and 29 females, aged from 48 to 69 with an average of (58.22±5.10) years, with a disease duration ranged from 1 to 9 years with an average of(5.10±1.55) years. The perioperative indicators, complications, and knee joint recovery rate were statistically analyzed for both groups, as well as the preoperative and postoperative coagulation function, fibrinogen (FIB), D-dimer (D-D), gait parameters (step frequency, step length, step speed), biomechanical indicators, weight bearing line (WBL), medial proximal tibial angle (MPTA), joint line conergence angle (JLCA), and anterior cruciate ligament (ACL) function (body width, tibial anterior displacement). RESULTS: All patients were followed up for 6 months. The intraoperative blood loss, operation time, and number of fluoroscopic views in the observation group were (358.58±93.76) ml, (84.42±8.17) min, and (2.00±0.44) times, respectively, which were all less than those in the control group (465.55±105.38) ml, (96.53±10.51) min, and (6.31±0.58) times (P<0.05). Three days after surgery, the FIB and D-D levels in the observation group were (4.21±0.48) g·L^-1 and (204.47±35.59) μg·L^-1, respectively, which were both lower than those in the control group (5.56±0.57) g·L^-1 and (311.12±42.23) μg·L^-1 (P<0.05). Three months after surgery, the step frequency, step length, and step speed in the observation group were (1.89±0.23) steps·s^-1, (0.57±0.15) m, and (0.99±0.11) m·s^-1, respectively, which were all higher than those in the control group (1.80±0.18) steps·s^-1, (0.50±0.14) m, and (0.95±0.09) m·s^-1 (P<0.05). Three months after surgery, the WBL and MPTA in the observation group were (45.53±4.41)% and (87.03±8.15)°, respectively, which were both higher than those in the control group (38.38±4.36)% and (83.68±8.50)°, and the JLCA was (2.36±0.24)°, which was lower than that in the control group (2.61±0.33)° (P<0.05). The ACL body width during internal fixation removal was (5.60±0.51) mm, which was greater than that in the control group (5.08±0.56) mm, and the tibial migration was (5.70±0.42) mm, which was less than that in the control group (6.33±0.48) mm (P<0.05). There was no significant difference in the incidence of complications between the two groups (P>0.05). Six months after surgery, there was no significant difference in the recovery rate of knee joint between the two groups (P>0.05). CONCLUSION The application of personalized osteotomy guide technique in MOWHTO can help improve knee biomechanics and ACL function, and has less effect on coagulation function and no increase in complications.
Humans ; Male ; Female ; Osteotomy/methods* ; Middle Aged ; Tibia/physiopathology* ; Aged ; Biomechanical Phenomena ; Retrospective Studies ; Osteoarthritis, Knee/physiopathology*

OBJECTIVE: As an age-related neurodegenerative disease, the prevalence of mild cognitive impairment (MCI) increases with age. Within the framework of traditional Chinese medicine, spleen-kidney deficiency syndrome (SKDS) is recognized as the most frequent MCI subtype. Due to the covert and gradual onset of MCI, in community settings it poses a significant challenge for patients and their families to discern between typical aging and pathological changes. There exists an urgent need to devise a preliminary diagnostic tool designed for community-residing older adults with MCI attributed to SKDS (MCI-SKDS). METHODS: This investigation enrolled 312 elderly individuals diagnosed with MCI, who were randomly distributed into training and test datasets at a 3:1 ratio. Five machine learning methods, including logistic regression (LR), decision tree (DT), naive Bayes (NB), support vector machine (SVM), and gradient boosting (GB), were used to build a diagnostic prediction model for MCI-SKDS. Accuracy, sensitivity, specificity, precision, F1 score, and area under the curve were used to evaluate model performance. Furthermore, the clinical applicability of the model was evaluated through decision curve analysis (DCA). RESULTS: The accuracy, precision, specificity and F1 score of the DT model performed best in the training set (test set), with scores of 0.904 (0.845), 0.875 (0.795), 0.973 (0.875) and 0.973 (0.875). The sensitivity of the training set (test set) of the SVM model performed best among the five models with a score of 0.865 (0.821). The area under the curve of all five models was greater than 0.9 for the training dataset and greater than 0.8 for the test dataset. The DCA of all models showed good clinical application value. The study identified ten indicators that were significant predictors of MCI-SKDS. CONCLUSION The risk prediction index derived from machine learning for the MCI-SKDS prediction model is simple and practical; the model demonstrates good predictive value and clinical applicability, and the DT model had the best performance. Please cite this article as: Ai YT, Zhou S, Wang M, Zheng TY, Hu H, Wang YC, Li YC, Wang XT, Zhou PJ. Development of a machine learning-based risk prediction model for mild cognitive impairment with spleen-kidney deficiency syndrome in the elderly. J Integr Med. 2025; 23(4): 390-397.
Humans ; Cognitive Dysfunction/diagnosis* ; Aged ; Male ; Female ; Machine Learning ; Spleen ; Aged, 80 and over ; Kidney ; Medicine, Chinese Traditional

Objective:To construct and validate the best predictive model for 28-day death risk in patients with septic shock based on different supervised machine learning algorithms.Methods:The patients with septic shock meeting the Sepsis-3 criteria were selected from Medical Information Mart for Intensive Care-Ⅳ v2.0 (MIMIC-Ⅳ v2.0). According to the principle of random allocation, 70% of these patients were used as the training set, and 30% as the validation set. Relevant predictive variables were extracted from three aspects: demographic characteristics and basic vital signs, serum indicators within 24 hours of intensive care unit (ICU) admission and complications possibly affecting indicators, functional scoring and advanced life support. The predictive efficacy of models constructed using five mainstream machine learning algorithms including decision tree classification and regression tree (CART), random forest (RF), support vector machine (SVM), linear regression (LR), and super learner [SL; combined CART, RF and extreme gradient boosting (XGBoost)] for 28-day death in patients with septic shock was compared, and the best algorithm model was selected. The optimal predictive variables were determined by intersecting the results from LASSO regression, RF, and XGBoost algorithms, and a predictive model was constructed. The predictive efficacy of the model was validated by drawing receiver operator characteristic curve (ROC curve), the accuracy of the model was assessed using calibration curves, and the practicality of the model was verified through decision curve analysis (DCA).Results:A total of 3?295 patients with septic shock were included, with 2?164 surviving and 1?131 dying within 28 days, resulting in a mortality of 34.32%. Of these, 2?307 were in the training set (with 792 deaths within 28 days, a mortality of 34.33%), and 988 in the validation set (with 339 deaths within 28 days, a mortality of 34.31%). Five machine learning models were established based on the training set data. After including variables at three aspects, the area under the ROC curve (AUC) of RF, SVM, and LR machine learning algorithm models for predicting 28-day death in septic shock patients in the validation set was 0.823 [95% confidence interval (95% CI) was 0.795-0.849], 0.823 (95% CI was 0.796-0.849), and 0.810 (95% CI was 0.782-0.838), respectively, which were higher than that of the CART algorithm model (AUC = 0.750, 95% CI was 0.717-0.782) and SL algorithm model (AUC = 0.756, 95% CI was 0.724-0.789). Thus above three algorithm models were determined to be the best algorithm models. After integrating variables from three aspects, 16 optimal predictive variables were identified through intersection by LASSO regression, RF, and XGBoost algorithms, including the highest pH value, the highest albumin (Alb), the highest body temperature, the lowest lactic acid (Lac), the highest Lac, the highest serum creatinine (SCr), the highest Ca 2+, the lowest hemoglobin (Hb), the lowest white blood cell count (WBC), age, simplified acute physiology score Ⅲ (SAPSⅢ), the highest WBC, acute physiology score Ⅲ (APSⅢ), the lowest Na +, body mass index (BMI), and the shortest activated partial thromboplastin time (APTT) within 24 hours of ICU admission. ROC curve analysis showed that the Logistic regression model constructed with above 16 optimal predictive variables was the best predictive model, with an AUC of 0.806 (95% CI was 0.778-0.835) in the validation set. The calibration curve and DCA curve showed that this model had high accuracy and the highest net benefit could reach 0.3, which was significantly outperforming traditional models based on single functional score [APSⅢ score, SAPSⅢ score, and sequential organ failure assessment (SOFA) score] with AUC (95% CI) of 0.746 (0.715-0.778), 0.765 (0.734-0.796), and 0.625 (0.589-0.661), respectively. Conclusions:The Logistic regression model, constructed using 16 optimal predictive variables including pH value, Alb, body temperature, Lac, SCr, Ca 2+, Hb, WBC, SAPSⅢ score, APSⅢ score, Na +, BMI, and APTT, is identified as the best predictive model for the 28-day death risk in patients with septic shock. Its performance is stable, with high discriminative ability and accuracy.

This study investigated the effect of different carrier materials on the in vitro properties of progesterone solid dispersions. The solid dispersions of the insoluble drug progesterone were prepared by hot melt extrusion technique using rheological properties as the index of investigation, and the in vitro properties of the solid dispersions were characterized. Scanning electron microscope revealed solid dispersions with rough surfaces and agglomerated microstructures into irregular lumpy particles. Differential scanning calorimetry and powder X-ray diffraction showed the change of progesterone crystalline form in solid dispersions from crystalline to amorphous state. In vitro dissolution studies showed that solid dispersions prepared with different carrier materials can effectively improve the dissolution rate of drugs. The results of the study showed that the type of carrier material had a significant effect on the in vitro properties of solid dispersions, providing a reference for the study of solid dispersions in the controlled release of insoluble drugs.

Needle-free injection technology (NFIT) refers to the drug delivery systems in which drugs are propelled as high-speed jet streams using any of the pressure source to penetrate the skin to the required depth. NFIT is a promising drug delivery system as it enables the injection of liquids, powders, and depot/projectiles, and has the advantages of preventing needle stick accidents, improving drug bioavailability, eliminating needle-phobia, increasing vaccine immunity, simplifying operations and is convenient for patients to use. NFIT and its research background, the structure and classification of needle-free jet injectors (NFJI), drugs that can be delivered using NFJI and the factors affecting the injection effect are comprehensively reviewed in this paper. The limitations and potential development directions are summarized to provide a theoretical basis for the application and development of NFIT.