Prescription optimization is a crucial aspect in the study of traditional Chinese medicine （TCM） compounds. In recent years， the introduction of mathematical methods， data mining techniques， and artificial neural networks has provided new tools for elucidating the compatibility rules of TCM compounds. The study of TCM compounds involves numerous variables， including the proportions of different herbs， the specific extraction parts of each ingredient， and the interactions among multiple components. These factors together create a complex nonlinear dose-effect relationship. In this context， it is essential to identify methods that suit the characteristics of TCM compounds and can leverage their advantages for effective application in new drug development. This paper provided a comprehensive review of the cutting-edge optimization experimental design methods applied in recent studies of TCM compound compatibilities. The key technical issues， such as the optimization of source material selection， dosage optimization of compatible herbs， and multi-objective optimization indicators， were discussed. Furthermore， the evaluation methods for component effects were summarized during the optimization process， so as to provide scientific and practical foundations for innovative research in TCM and the development of new drugs based on TCM compounds.

[Objective] : To systematically evaluate the overall efficacy of external application of traditional Chinese medicine (EA-TCM) in combination with oral three-step analgesic ladder therapy for patients suffering from cancer-induced bone pain (CIBP). [Methods] : We conducted a literature search of randomized controlled trials on the combination of EA-TCM and three-step analgesic ladder therapy for CIBP across ten databases and two registration systems. It included four Chinese databases [Chinese Biomedical Literature Database (SinoMed), China National Knowledge Infrastructure (CNKI), Wanfang Database, and China Science and Technology Journal Database (VIP) ], six English databases (Scopus, Embase, Web of Science, PubMed, Cochrane Library, and OpenGrey), and two registration systems (Chinese Clinical Trial Registry and ClinicalTrials.gov). The timeframe for the literature search extended from the inception of each database to December 31, 2023. Meta-analysis was performed using RevMan (v5.4.1), and the outcome indicators (pain relief rate, analgesic duration, quality of life, pain intensity, breakthrough pain frequency, and adverse reactions) were graded using GRADE profiler (v3.6). [Results] : According to the established inclusion and exclusion criteria, a total of 43 studies was deemed eligible, involving 3 142 participants with CIBP. The results of meta-analysis showed that compared with oral three-step analgesic ladder therapy alone, the combined therapy of EA-TCM and three-step analgesic ladder has a significant improvement in pain relief rate [risk ratio (RR) = 1.32, 95% confidence interval (CI): 1.24 to 1.41, P < 0.000 01], analgesic duration [mean difference (MD) = 1.33, 95% CI: 0.97 to 1.69, P < 0.000 01], and quality of life (MD = 5.66, 95% CI: 4.88 to 6.44, P < 0.000 01). Furthermore, the combined therapy significantly reduced pain intensity (MD = – 1.00, 95% CI: – 1.19 to – 0.80, P < 0.000 01), breakthrough pain frequency (MD = – 0.43, 95% CI: – 0.51 to – 0.36, P < 0.000 01), and adverse reactions (RR = 0.60, 95% CI: 0.53 to 0.68, P < 0.000 01) in CIBP patients. Based on the GRADE assessment, the level of evidence varied from low to moderate. [Conclusion] EA-TCM combined with the three-step analgesic ladder therapy can effectively alleviate pain symptoms in patients with CIBP and improve their quality of life. Additionally, the EA-TCM can effectively reduce the incidence of adverse reactions associated with three-step analgesic therapy.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Objective: To examine the impacts of chemotherapy/radiotherapy, targeted therapy, and combined treatment on the quality of life in patients with intermediate and advanced lung cancer. Methods: The patients with intermediate and advanced lung cancer undergoing chemotherapy/radiotherapy, targeted therapy, and combined treatment for the first time were recruited from a tertiary hospital in Weifang City, Shandong Province, using a quota sampling method in September 2023. Basic information was collected using a general information questionnaire, and the quality of life was assessed using the Chinese version of Functional Assessment of Cancer Therapy-General. The investigation started on the 7th day of treatment, and the follow-ups were conducted at 3 and 6 months. The quality of life in patients with different treatment regimens and at different treatment time were compared using repeated measure analysis of variance. Results: There were 26 chemotherapy/radiotherapy patients, 32 targeted therapy patients, and 95 combination therapy patients. There were no significant differences in age, gender, place of residence, education level, self-rated economic status, medical insurance, pathological type and disease stage among the three treatment regimens (all P>0.05). The repeated measure analysis of variance showed an interaction effect between time and group among patients receiving the three treatment regimens (P<0.05). The quality of life scores of patients receiving combination therapy decreased with extended treatment time (all P<0.05). The quality of life scores of patients receiving targeted therapy at 3 and 6 months were lower than those treated for 7 days (both P<0.05). No significant differences were observed in quality of life scores among chemotherapy/radiotherapy patients with different treatment durations (all P>0.05). At 3 and 6 months, patients receiving combination therapy had lower quality of life scores compared to those receiving chemotherapy/radiotherapy or targeted therapy (all P<0.05). Conclusion The decline in quality of life for patients with intermediate and advanced lung cancer undergoing chemotherapy/radiotherapy and targeted therapy is less than that for patients receiving combined therapy.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

OBJECTIVE: To explore the construction method of a resistant multiple myeloma (MM) patient-derived xenotransplantation (PDX) model. METHODS: 1.0×10⁷ MM patient-derived mononuclear cells (MNCs), 2.0×10⁶ MM.1S cells and 2.0×10⁶ NCI-H929 cells were respectively subcutaneously inoculated into NOD.CB17-Prkdc^scid Il2rg^tm1/Bcgen (B-NDG) mice with a volume of 100 μl per mouse to establish mouse model. The morphologic, phenotypic, proliferative and genetic characteristics of PDX tumor were studied by hematoxylin-eosin staining, immunohistochemical staining (IHC), cell cycle analysis, flow cytometry and fluorescence in situ hybridization (FISH). The sensitivity of PDX tumor to bortezomib and anlotinib monotherapy or in combination was investigated through cell proliferation, apoptosis and in vitro and in vivo experiments. The effects of anlotinib therapy on tumor blood vessel and cell apoptosis were analyzed by IHC, TUNEL staining and confocal fluorescence microscope. RESULTS: MM PDX model was successfully established by subcutaneously inoculating primary MNCs. The morphologic features of tumor cells from MM PDX model were similar to those of mature plasma cells. MM PDX tumor cells positively expressed CD138 and CD38, which presented 1q21 amplification, deletion of Rb1 and IgH rearrangement, and had a lower proliferative activity than MM cell lines. in vitro, PDX, MM.1S and NCI-H929 cells were treated by bortezomib and anlotinib for 24 hours, respectively. Cell viability assay showed that the IC₅₀ value of bortezomib were 5 716.486, 1.025 and 2.775 nmol/L, and IC₅₀ value of anlotinib were 5 5107.337, 0.706 and 5.13 μmol/L, respectively. Anlotinib treatment increased the apoptosis of MM.1S cells (P < 0.01), but did not affect PDX tumor cells (P >0.05). in vivo, there was no significant difference in PDX tumor growth between bortezomib monotherapy group and control group (P >0.05), while both anlotinib monotherapy and anlotinib combined with bortezomib effectively inhibited PDX tumor growth (both P < 0.05). The vascular perfusion and vascular density of PDX tumor were decreased in anlotinib treatment group (both P < 0.01). The apoptotic cells in anlotinib treatment group were increased compared with those in control group (P < 0.05). CONCLUSION Bortezomib-resistant MM PDX model can be successfully established by subcutaneous inoculation of MNCs from MM patients in B-NDG mice. This PDX model, which retains the basic biological characteristics of MM cells, can be used to study the novel therapies.
Animals ; Bortezomib ; Humans ; Multiple Myeloma/pathology* ; Mice ; Apoptosis ; Drug Resistance, Neoplasm ; Cell Line, Tumor ; Xenograft Model Antitumor Assays ; Mice, Inbred NOD ; Disease Models, Animal ; Cell Proliferation ; Transplantation, Heterologous

Background/Aims: The purpose of this study was to develop a diagnostic model utilizing multimodal ultrasound parameters to aid in the detection of cervical lymph node metastasis in papillary thyroid cancer (PTC) patients. Methods: The study included 84 suspicious lymph nodes from 69 PTC patients, all of whom underwent fine needle aspiration with pathological results. Data from conventional grayscale ultrasound, shear wave elastography (SWE), and superb microvascular imaging were analyzed. Key ultrasound features were compared between benign and metastatic groups to create a diagnostic model using Fisher’s stepwise discriminant analysis. The model’s effectiveness was assessed with self-testing, cross-validation, and receiver operating characteristic curve analysis. Results: Four features, namely lymphatic hilum (X1), cortical hyperechogenicity (X2), vascular pattern (X4), and SWEmean (X7), were integral to the discriminant analysis, resulting in the equation: Y1 = -3.461 + 2.423X1 + 0.321X2 + 1.620X4 + 0.109X7, Y2 = -8.053 + 0.414X1 + 2.600X2 + 2.504X4 + 0.192X7. If Y1 < Y2, the LN would be diagnosed as metastatic lymph nodes. The model demonstrated an area under the curve of 0.833, with a sensitivity of 83.33% and specificity of 83.33%. Conclusions The multimodal ultrasound diagnostic model, established through Fisher’s stepwise discriminant analysis, proved effective in identifying metastatic lymph nodes in PTC patients.

ObjectiveTo analyze the epidemiological characteristics of long COVID and to investigate its main influencing factors by examining individuals infected with SARS-CoV-2 between March and June 2022 in two communities in Shanghai, to lay the foundation for further research on the mechanism and clinical treatment of long COVID, and to provide the basis for the development of inexpensive, convenient, and feasible prevention and intervention strategies. MethodsA cross-sectional study was conducted, enrolling 6 410 individuals infected with SARS-CoV-2. Data were collected through a questionnaire survey. The incidence and common symptoms of long COVID were analyzed, along with their associations with demographic characteristics, medical history, and behavioral factors. A logistic regression model was used to identify the major factors associated with the development of long COVID symptoms. ResultsThe overall incidence rate of long COVID among the study population was 13.9%. The most commonly reported symptoms included fatigue (65.1%), attention disorders (23.1%), and cough (16.9%). The analysis showed that having underlying chronic diseases (OR=2.580, 95%CI: 2.165‒3.074), a history of allergies (OR=1.418, 95%CI: 1.003‒1.971), current smoking (OR=1.461, 95%CI: 1.013‒2.079), ever smoking (OR=2.462, 95%CI: 1.687‒3.551), a greater number of symptoms during the acute phase [1 symptom (OR=1.778, 95%CI: 1.459‒2.162), 2 symptoms (OR=2.749, 95%CI: 2.209‒3.409), ≥3 symptoms (OR=7.792, 95%CI: 6.333‒9.593)] and aggravated symptoms during the acute phase (OR=1.082, 95%CI: 1.070‒1.094) were factors associated with a higher risk of developing long COVID symptoms. Additionally, individuals who had consumed alcohol in the past year (OR=1.914, 95%CI: 1.344‒2.684) were more prone to objective long COVID symptoms. Among individuals under 50 years of age, females (OR=1.427, 95%CI: 1.052‒1.943) were more likely to develop objective long COVID symptoms. ConclusionThis study has identified the diversity of long COVID symptoms, which involve multiple organs and systems, including fatigue, attention disorders, cough, and joint pain. It has also revealed associations between long COVID and various demographic factors (e.g., age, gender), personal medical history (e.g., underlying chronic diseases, history of allergies), acute-phase characteristics (e.g., number and severity of symptoms), and behavioral factors (e.g., smoking, alcohol consumption). These findings highlight the need for further research and ongoing surveillance of long COVID and may inform the development of more targeted health management strategies for specific populations.