With the continuous deepening of the concept of precision diagnosis and treatment for lung cancer, how to achieve higher efficiency and accuracy in the screening, diagnosis, and treatment pathways in clinical practice has become an important issue that urgently needs to be overcome. The current clinical difficulty lies in the fact that despite continuous advancements in imaging and molecular diagnostic technologies, there are still limitations in manual efficiency and subjective experience when it comes to massive data analysis and multi-scale feature extraction. Artificial intelligence (AI), especially algorithm systems based on deep learning, is an innovative technology capable of deeply empowering medical big data. This method utilizes algorithms such as convolutional neural networks, combined with radiomics, pathomics, and multi-modal data fusion analysis, demonstrating immense potential in early precise detection and benign-malignant differentiation of pulmonary nodules, digital pathological subtype recognition and non-invasive prediction of driver genes, precise 3D surgical planning and automatic delineation of radiotherapy target volumes, as well as dynamic risk warning during follow-up. This innovative technology provides a brand-new solution for realizing intelligent and individualized lung cancer diagnosis and treatment models. This consensus, based on the latest evidence from evidence-based medicine and combined with the development trends in the AI field and real-world clinical needs, was ultimately formed by gathering the consensus opinions of multidisciplinary experts in radiology, pathology, thoracic surgery, and other fields. The main content covers the application specifications of AI in the three core scenarios of lung cancer screening, diagnosis, and treatment, the technical standards for data collection and algorithm validation, as well as the ethical and regulatory challenges faced at the current stage. It aims to clarify the applicable boundaries of AI as a clinical auxiliary decision support tool, providing scientific guidance and standardized exploration directions for peers currently engaged in or planning to carry out AI-assisted clinical diagnosis, treatment, and translation of lung cancer.

Hemoglobin (Hb) concentration is a key clinical biomarker for diagnosing and managing anemia, ischemic stroke, perioperative blood loss, and chronic diseases such as renal failure. Traditional venous blood sampling remains the gold standard due to its high accuracy, but its invasive nature limits frequent testing, real time monitoring, and large scale screening. This has driven growing interest in non-invasive Hb detection technologies over the past decade. Among these, optical methods are the most promising because of their safety, potential for continuous monitoring, and compatibility with portable or wearable devices. This paper systematically reviews major advances in optical non invasive Hb detection from the last ten years. We focus on near-infrared spectroscopy branches—photoplethysmography (PPG) and dynamic spectrum (DS)—and also cover color analysis/RGB imaging, Raman spectroscopy, and photoacoustic spectroscopy. For each technology, we explain its detection principles, analyze advantages and limitations, and summarize optimization strategies reported in recent literature. PPG, based on pulsatile blood volume changes, underpins many commercial continuous monitors. However, its accuracy is constrained by motion artifacts, individual physiological variations (e.g., skin tone, tissue thickness), and low AC signal to noise ratio. In contrast, DS—an advanced derivative of PPG—uses a differential principle to extract absorbance changes between systolic and diastolic peaks. This theoretically eliminates interference from static tissues (skin, bone, venous blood) and common mode noise (e.g., ambient light), positioning DS as a more robust framework for high precision Hb quantification. Beyond spectral methods, color analysis/RGB imaging offers a hardware minimalist approach. By analyzing images of vascular rich, thin tissues (e.g., conjunctiva, nail beds, palms), it enables Hb estimation using smartphone cameras. Recent advances have shifted from manual RGB feature extraction to deep learning models and spectral super resolution that reconstruct hyperspectral data from RGB inputs, significantly improving screening accuracy. Our academic perspective emphasizes critical and integrative analysis. We highlight persistent challenges that hinder clinical translation: profound individual biological variability (skin optics, microvascular architecture), sensitivity to measurement conditions (pressure, ambient light), and a lack of standardized validation protocols and multi center trials. A central thesis is that no single optical method is universally superior; each involves trade offs between accuracy, complexity, cost, and practicality. Looking forward, we posit that the next performance leap will come from multimodal information fusion—combining PPG, electrocardiogram (ECG), bioimpedance, or different optical modalities to compensate for individual differences and environmental noise. AI and deep learning are essential not only for image analysis but also for automated, end to end feature extraction from complex waveforms like PPG sequences. Advancing hardware (tunable lasers, quantum dot LEDs, novel sensor designs) is crucial to improve signal fidelity and portability. Finally, we advocate for clinical scenario specific optimization and rigorous standardized evaluation frameworks to gain regulatory approval (e.g., FDA, NMPA) and achieve widespread clinical acceptance. In conclusion, this review synthesizes a decade of progress. Optical non-invasive Hb detection has evolved from proof of concept studies to emerging products and validated screening tools, but the journey toward reliable, clinic ready quantitative devices continues. The convergence of smarter algorithms, fused sensing modalities, and focused clinical validation offers the most promising path to transform this potential into routine medical practice, ultimately enabling personalized, continuous, and accessible hematological management.

Objective To examine the potential causal associations between different dietary taste preferences and the risk of prostatitis, so as to provide reference for the primary prevention of this disease. Methods Summary-level genome-wide association study(GWAS)data on five taste preferences-sweet, sour, salty, bitter and spicy—were obtained from the GWAS, and GWAS data for prostatitis were sourced from the FinnGen database. The potential causal associations between the dietary taste preferences and risk of prostatitis were analyzed with MR. The inverse variance weighted(IVW)method served as the primary analytical approach. Heterogeneity was assessed using Cochran's Q test. Horizontal pleiotropy was evaluated via the MR-Egger intercept and Mendelian randomization pleiotropy residual sum and outlier(MR-PRESSO)tests. Leave-one-out analysis was performed to evaluate the influence of individual single nucleotide polymorphisms(SNPs)on the causal estimates. Results Preferences for sweet(OR=1.338, 95%CI:1.024-1.748, P=0.033)and salty(OR=1.356, 95%CI:1.092-1.684, P=0.006)tastes showed positive causal associations with the risk of prostatitis, whereas a preference for bitter taste was negatively associated(OR=0.638, 95%CI:0.416-0.978, P=0.039). No significant causal links were observed for sour or spicy taste preferences(P＞0.05). Sensitivity analyses and reverse MR analyses further supported the robustness of the findings.Conclusion Sweet and salty taste preferences may be potential dietary risk factors for prostatitis, while bitter taste preference could play a protective role. Further research is needed to elucidate these causal relationships, which may contribute to developing tailored dietary intervention strategies for prostatitis prevention.

With the accelerating trend of population aging, the number of elderly patients with lung cancer continues to rise, and the disease burden is becoming increasingly heavy. The clinical management of these patients faces severe challenges due to their decreased physiological reserve, complex comorbidities, and significant individual heterogeneity. Consequently, under traditional diagnosis and treatment models, doctors often struggle to identify the individualized risks of elderly patients in a timely and comprehensive manner, which can easily lead to decision biases such as undertreatment or overtreatment. In view of this, this study advocates for the establishment of an umbrella decision-making model specifically tailored for elderly lung cancer patients. Grounded in a multidisciplinary team (MDT) platform, this model deeply integrates oncological indicators with the comprehensive geriatric assessment (CGA) system. By holistically considering multidimensional variables including tumor burden, organ function, frailty index, cognitive status, and social support, the model establishes an operational mechanism characterized by "single entry, precise stratification, and targeted selection". Accordingly, patients can be scientifically triaged into distinct intervention tiers, such as active surveillance, minimally invasive surgery, drug therapy, radiotherapy, and best supportive care, thereby achieving real-time alignment between treatment intensity and patient fitness. This article elaborates on the construction logic and key operational procedures of this novel decision-making framework, aiming to guide clinical practice beyond the limitations of a tumor-centric perspective toward a holistic, dynamic, whole-course management strategy. This transition seeks to ensure optimal quality of life and clinical net benefit for elderly patients alongside survival prolongation.

Electromagnetic fields can regulate the fundamental biological processes involved in bone remodeling. As a non-invasive physical therapy, electromagnetic fields with specific parameters have demonstrated therapeutic effects on bone remodeling diseases, such as fractures and osteoporosis. Electromagnetic fields can be generated by the movement of charged particles or induced by varying currents. Based on whether the strength and direction of the electric field change over time, electromagnetic fields can be classified into static and time-varying fields. The treatment of bone remodeling diseases with static magnetic fields primarily focuses on fractures, often using magnetic splints to immobilize the fracture site while studying the effects of static magnetic fields on bone healing. However, there has been relatively little research on the prevention and treatment of osteoporosis using static magnetic fields. Pulsed electromagnetic fields, a type of time-varying field, have been widely used in clinical studies for treating fractures, osteoporosis, and non-union. However, current clinical applications are limited to low-frequency, and research on the relationship between frequency and biological effects remains insufficient. We believe that different types of electromagnetic fields acting on bone can induce various “secondary physical quantities”, such as magnetism, force, electricity, acoustics, and thermal energy, which can stimulate bone cells either individually or simultaneously. Bone cells possess specific electromagnetic properties, and in a static magnetic field, the presence of a magnetic field gradient can exert a certain magnetism on the bone tissue, leading to observable effects. In a time-varying magnetic field, the charged particles within the bone experience varying Lorentz forces, causing vibrations and generating acoustic effects. Additionally, as the frequency of the time-varying field increases, induced currents or potentials can be generated within the bone, leading to electrical effects. When the frequency and power exceed a certain threshold, electromagnetic energy can be converted into thermal energy, producing thermal effects. In summary, external electromagnetic fields with different characteristics can generate multiple physical quantities within biological tissues, such as magnetic, electric, mechanical, acoustic, and thermal effects. These physical quantities may also interact and couple with each other, stimulating the biological tissues in a combined or composite manner, thereby producing biological effects. This understanding is key to elucidating the electromagnetic mechanisms of how electromagnetic fields influence biological tissues. In the study of electromagnetic fields for bone remodeling diseases, attention should be paid to the biological effects of bone remodeling under different electromagnetic wave characteristics. This includes exploring innovative electromagnetic source technologies applicable to bone remodeling, identifying safe and effective electromagnetic field parameters, and combining basic research with technological invention to develop scientifically grounded, advanced key technologies for innovative electromagnetic treatment devices targeting bone remodeling diseases. In conclusion, electromagnetic fields and multiple physical factors have the potential to prevent and treat bone remodeling diseases, and have significant application prospects.

Hepatocellular carcinoma(HCC),which is essentially primary liver cancer,is closely related to CD8+T cell immune infiltration and immune suppression.We constructed a CD8+T cells related risk score model to pre-dict the prognosis of HCC patients and provided therapeutic guidance based on the risk score.Using integrated bulk RNA sequencing(RNA-seq)and single-cell RNA sequencing(scRNA-seq)datasets,we identified stable CD8+T cell signatures.Based on these signatures,a 3-gene risk score model,comprised of KLRB1,RGS2,and TN-FRSF1B was constructed.The risk score model was well validated through an independent external validation co-hort.We divided patients into high-risk and low-risk groups according to the risk score and compared the differ-ences in immune microenvironment between these two groups.Compared with low-risk patients,high-risk patients have higher M2-type macrophage content(P＜0.0001)and lower CD8+T cells infiltration(P＜0.0001).High-risk patients predict worse response to immunotherapy treatment than low-risk patients(P＜0.01).Drug sensitivity a-nalysis shows that PI3K-β inhibitor AZD6482 and TGFβRII inhibitor SB505124 may be suitable therapies for high-risk patients,while the IGF-1R inhibitor BMS-754807 or the novel pyrimidine-based anti-tumor metabolic drug Gemcitabine could be potential therapeutic choices for low-risk patients.Moreover,expression of these 3-gene mod-el was verified by immunohistochemistry.In summary,the establishment and validation of a CD8+T cell-derived risk model can more accurately predict the prognosis of HCC patients and guide the construction of personalized treatment plans.

Endometriosis presents a common challenge in global women's health.Ferroptosis,as an emerging form of regulated cell death,has recently attracted attention in relation to endometriosis.In terms of disease mechanisms,ferroptosis drives ovarian endometrial fibrosis via the induction of iron overload,and promotes disease progression by regulating multiple signaling pathways,such as p38 mitogen-activated protein kinase/signal transducer and activator of transcription 6(p38MAPK/STAT6),promote angiogenesis,autophagy,etc,fueling disease progression.In terms of infertility,ferroptosis affects embryo development and ovarian function,thereby reducing fertility.From a diagnostic perspective,high expression of ferroptosis-related genes provides potential biomarkers for the early diagnosis of endometriosis,effectively distinguishing patients from healthy individuals,and showing important clinical value.In terms of treatment strategies,non-natural compounds such as Erastin,as well as natural compounds such as resveratrol,ursolic acid,and baicalein,have shown potential therapeutic effects in relieving the pathological process and related symptoms of endometriosis.This review comprehensively elucidates the key role of ferroptosis in endometriosis in terms of the disease mechanisms,infertility,diagnosis,and treatment,and explores its potential as a diagnostic biomarker and therapeutic target,thus providing new theoretical support and treatment strategies for the management of endometriosis.

BACKGROUND:Hepatocyte-like cells induced by mesenchymal stem cells are promising seed cells for liver regeneration or liver tissue engineering.The efficiency of traditional two-dimensional culture for hepatocyte induction is low,and more and more research is focused on three-dimensional culture for inducing hepatocyte differentiation.OBJECTIVE:To summarize three-dimensional culture models for the hepatic induction of mesenchymal stem cells,focus on research progress on the epigenetic regulation mechanisms of mesenchymal stem cell hepatogenic differentiation,providing a theoretical basis for improving the differentiation efficiency of mesenchymal stem cells.METHODS:Relevant articles in the PubMed and other databases such as CNKI were searched,using Chinese and English search terms"mesenchymal stem cell,3D culture,hepatogenic differentiation,hepatocyte-like cells,epigenetics."Additionally,the literature tracing method was employed to find some of the literature for a comprehensive review and analysis.RESULTS AND CONCLUSION:(1)Common three-dimensional culture models for the hepatogenic differentiation of mesenchymal stem cells currently include spheroids,biological scaffolds,bioprinting,and microfluidic chips.Each of these models has its own advantages and disadvantages in the process of inducing hepatogenic differentiation.(2)During the differentiation of mesenchymal stem cells into hepatocyte-like cells,epigenetic regulation plays a key role,primarily involving histone modification,DNA methylation,and the regulation of non-coding RNAs.(3)Under three-dimensional culture conditions,epigenetic modifications,especially histone acetylation,play an important role in promoting the hepatogenic differentiation of mesenchymal stem cells.

Objective:To explore the feasibility and preliminary clinical experience of robot-assisted pelvic exenteration (PE) in the treatment of locally advanced (LARC) and recurrent (LRRC) rectal cancer.Method:A descriptive case series research method was adopted. Inclusion criteria included: (1) Age 18-80 years old; (2) Preoperative puncture biopsy performed through endoscopy, and a pathological diagnosis of rectal malignant tumor; (3) Preoperative imaging examinations confirming locally advanced (cT4b stage) or locally recurrent rectal cancer, with tumor location in the pelvic cavity; (4) Physical condition: ECOG score ≤1 point, and radical resection being feasible after assessment. The data for five patients with LARC or LRRC who underwent pelvic exenteration (PE) using the da Vinci robotic surgical system in the Department of Anorectal Surgery, the Second Affiliated Hospital of Naval Medical University (Shanghai Changzheng Hospital) from January, 2024 to January, 2025 were collected retrospectively. The mean age was (46.8±7.7) years, with 3 males and 2 females, who comprised 2 cases of LARC and 3 cases of LRRC. Two patients received preoperative radiotherapy, and 4 patients received preoperative chemotherapy. The average body mass index was (21.5±2.7) kg/m2. According to the American Society of Anesthesiologists (ASA) classification, 2 cases were grade II and 3 cases were grade III.Results:All patients with LARC or LRRC successfully underwent robot-assisted PE. The average operation time was (496.4±139.5) minutes; the average intraoperative blood loss was (72.0±29.5) ml; the average postoperative exhaust time was (50.0 ±13.6) hours; and the average postoperative defecation time was(64.2±15.3) hours. Mean early postoperative VAS pain scores was (3.6±1.5) points. Three patients underwent primary intestinal anastomosis, and 2 patients underwent colonic single-lumen ostomy. All 5 patients underwent urinary system reconstruction, among which 2 underwent ureterovesical reimplantation, 1 underwent percutaneous ureterostomy, 1 underwent ileal conduit replacement of bladder, and 1 underwent direct bladder suture. After surgery, except for 1 case of pelvic infection with effusion (Clavien-Dindo grade IIIa), there were no obvious postoperative complications. Postoperative pathological results showed that all patients achieved R0 resection, including 1 case of T4a stage and 4 cases of T4b stage (all involving urogenital organs or tissues), and 3 cases of N0 stage and 2 cases of N1 stage, with a maximum tumor diameter of (4.7±1.9) cm. The median postoperative follow-up time was 11 (range 7 to 17) months, and no patient experienced local recurrence.Conclusion:The above short-term preliminary results of robot-assisted PE in the treatment of LARC or LRRC within the pelvic cavity indicate that it is both safe and feasible.

Objective To investigate the mechanism of benzyl isothiocyanate(BITC)combined with sorafenib(Sor)in the treatment of anaplastic thyroid cancer(ATC).Methods Two ATC cell lines,8505C and CAL-62,were treated with Sor at concentrations of 0,20,30,40,and 50 μmol/L.The cell survival rate was assessed using CCK-8 assay.The combined dose of BITC and Sor was determined by calculating combination index(CI).CAL-62 and 8505C cells were exposed to 10 μmol/L BITC(BITC group),10 μmol/L Sor(Sor group),or a combination of 10 μmol/L BITC and 10 μmol/L Sor(BITC+Sor group)for 24 hours.The control group was not treated.The effects of Sor and BITC on ATC cell viability were evaluated using the CCK-8 method.Apoptosis was analyzed via flow cytometry.Western blot assay was employed to detect the protein expression levels of LC3B Ⅱ,Beclin-1 and nuclear factor(NF)-κB.Real-time fluorescence quantitative PCR was used to quantify the mRNA levels of LC3B.Additionally,CAL-62 cells were subcutaneously injected into mice to establish tumor xenograft model.Mice were treated with BITC(100 mg/kg,intraperitoneal injection),Sor(30 mg/kg,intragastric administration)or a combination of BITC and Sor every other day for 21 days.Finally,the expression levels of LC3B Ⅱ,Beclin-1 and NF-κB in tumor tissue were analyzed by Western blot assay.Results Sor significantly inhibited the viability of CAL-62 and 8505C cells in a concentration-dependent manner.The combination index(CI)was 0.710 at BITC 10 μmol/L and Sor 10 μmol/L,indicating a moderate synergistic effect between the two drugs.In both 8505C and CAL-62 cells,compared with the control group,treatment with BITC or Sor resulted in the decreased cell viability,as well as reduced expression levels of Beclin-1 and NF-κB proteins(P＜0.05),and the apoptosis rate,LC3B mRNA and LC3B Ⅱ protein expression levels were significantly increased(P＜0.05).When BITC and Sor were combined,the cell viability,Beclin-1 and NF-κB protein expressions were further reduced compared to either drug alone,while the apoptosis rate,LC3B mRNA and LC3B Ⅱ protein expression levels were significantly elevated(P＜0.05).In the mouse xenograft tumor model,the BITC+Sor group exhibited increased LC3B Ⅱ expression,along with decreased Beclin-1 and NF-κB expression levels,tumor volume and tumor mass compared to the BITC or Sor groups(P＜0.05).Conclusion The combination of BITC and Sor can inhibit ATC cells through NF-κB pathway,induce autophagy and promote apoptosis in vitro and in vivo.