Objective To discuss the value of clinical radiomic nomogram(CRN)and deep convolutional neural network(DCNN)in distinguishing atypical pulmonary hamartoma(APH)from atypical lung adenocarcinoma(ALA).Methods A total of 307 patients were retrospectively recruited from two institutions.Patients in institu-tion 1 were randomly divided into the training(n=184:APH=97,ALA=87)and internal validation sets(n=79:APH=41,ALA=38)in a ratio of 7∶3,and patients in institution 2 were assigned as the external validation set(n=44:APH=23,ALA=21).A CRN model and a DCNN model were established,respectively,and the performances of two models were compared by delong test and receiver operating characteristic(ROC)curves.A human-machine competition was conducted to evaluate the value of AI in the Lung-RADS classification.Results The areas under the curve(AUCs)of DCNN model were higher than those of CRN model in the training,internal and external validation sets(0.983 vs 0.968,0.973 vs 0.953,and 0.942 vs 0.932,respectively),however,the differences were not statistically significant(p=0.23,0.31 and 0.34,respectively).With a radiologist-AI com-petition experiment,AI tended to downgrade more Lung-RADS categories in APH and affirm more Lung-RADS cat-egories in ALA than radiologists.Conclusion Both DCNN and CRN have higher value in distinguishing APH from ALA,with the former performing better.AI is superior to radiologists in evaluating the Lung-RADS classification of pulmonary nodules.

Objective To explore the value of combined prediction model based on clinical and CT radiomics features in discriminating atypical pulmonary hamartoma(APH)from atypical lung adenocarcinoma(ALA).Methods A total of 290 patients with APH and ALA confirmed by pathology were retrospectively selected.250 patients from the First Affiliated Hospital of Anhui Medical University were randomly assigned into a training set(APH=91,ALA=84)and an internal validation set(APH=39,ALA=36)at a ratio of 7∶3,and other 40 patients from the First Affiliated Hospital of USTC were assigned as an external validation set(APH=21,ALA=19).The independent model and multivariate logistic regression combined model were constructed using the selected clinical-CT features and radiomics features,respectively,and a nomogram was drawn.Receiver operating characteristic(ROC)curve and DeLong test were used to evaluate and compare the performances of the models.Results The area under the curve(AUC)of the combined model established by 3 clinical-CT features and 4 radiomics features in the training set was 0.980,which was higher than that of clinical-CT model(AUC=0.885,P＜0.001)and radiomics model(AUC=0.975,P=0.042).The AUC of the combined model in the internal and external validation sets(0.963 vs 0.917)were also higher than those of clinical-CT model(0.858 vs 0.774)and radiomics model(0.953 vs 0.897),respectively.Conclusion The combined prediction model based on clinical and CT radiomics features can improve the differential diagnosis ability of APH and ALA.

Ferroptosis is a nonapoptotic form of cell death and differs considerably from the well-known forms of cell death in terms of cell morphology,genetics,and biochemistry.The three primary pathways for cell ferroptosis are system Xc-/glutathione peroxidase 4(GPX4),lipid metabolism,and ferric metabolism.Since the discovery of ferroptosis,mounting evidence has revealed its critical regulatory role in several diseases,especially as a novel potential target for cancer therapy,thereby attracting increasing attention in the fields of tumor biology and anti-tumor therapy.Accordingly,broad prospects exist for identifying ferroptosis as a potential therapeutic target.In this review,we aimed to systematically summarize the activation and defense mechanisms of ferroptosis,highlight the therapeutic targets,and discuss the design of nanomedicines for ferroptosis regulation.In addition,we opted to present the advantages and disadvantages of current ferroptosis research and provide an optimistic vision of future directions in related fields.Overall,we aim to provide new ideas for further ferroptosis research and inspire new strategies for disease diagnosis and treatment.

Objective:A dose verification system of two-dimensional semiconductor matrix(SRS MapCHECK)was used to verify the dose of the clinical treatment plan of patients who underwent CyberKnife(CK),which realized rapid verification for individualization of radiotherapy plans of patients through analyzed the γ-passing rates of them.Methods:A total of 253 patients with tumor who received CK clinical treatment in the First Medical Center of Chinese PLA General Hospital from March 2021 to May 2023 were selected.Among of them,121 cases received CK treatment on head,and 30 cases received that on lung,and 102 cases received CK treatment on abdomen and other metastatic tumor.In the MultiPlan treatment plan system,the plan of patient was mapped to the integrated model composed of StereoPHAN model and SRS MapCHECK matrix dose verification system by the means of the plan image center overlap.The dose verification was conducted on the plan of each patient on the basis of ensuring the consistency of the number of beam,direction of beam and the monitor unit.The different γ analysis standards(1%/1 mm,2%/1 mm,3%/1 mm,1%/2 mm,2%/2 mm,3%/2 mm,1%/3 mm,2%/3 mm and 3%/3 mm)were adopted to conduct global analysis of absolute dose for each verification plan,and the threshold(TH)of low dose was set as 10%.Results:The γ passing rates of phantom verification plans of 253 patients were respectively(88.64±5.91)%,(95.43±3.40)%,(97.90±2.06)%,(96.51±2.35)%,(98.15±1.68)%,(99.06±1.12)%,(98.30±1.39)%,(99.09±0.97)%and(99.52±0.63)%under different analysis standards.The γ passing rates of other standards of patients with tumor on different parts were larger than 95%except the analysis result of 1%1 mm standard.The overall analysis result of the deviation of central point dose was(-1.30±2.17)%,among of which the tumor of head,abdominal tumors and other metastatic tumor were about approximately-2%,while that of lung tumors were approximately-3%.The deviation of abdominal and other metastatic tumor was the minimum.The correlation analysis showed that the target volume and the size of the minimum collimator were respectively correlated to the dose deviation of the center.Conclusion:SRS MapCHECK dose verification system can conveniently and quickly realize the individualized verification for the plan of patients who receive CK treatment.

Intranasal drug delivery system is a non-invasive drug delivery route with the advantages of no first-pass effect, rapid effect and brain targeting. It is a feasible alternative to drug delivery via injection, and a potential drug delivery route for the central nervous system. However, the nasal physiological environment is complex, and the nasal delivery system requires "integration of medicine and device". Its delivery efficiency is affected by many factors such as the features and formulations of drug, delivery devices and nasal cavity physiology. Some strategies have been designed to improve the solubility, stability, membrane permeability and nasal retention time of drugs. These include the use of prodrugs, adding enzyme inhibitors and absorption enhancers to preparations, and new drug carriers, which can eventually improve the efficiency of intranasal drug delivery. This article reviews recent publications and describes the above mentioned aspects and design strategies for nasal intranasal drug delivery systems to provide insights for the development of intranasal drug delivery systems.
Administration, Intranasal ; Drug Delivery Systems ; Pharmaceutical Preparations ; Drug Carriers ; Brain ; Nasal Cavity/physiology* ; Nasal Mucosa

Dry powder inhalers (DPIs) had been widely used in lung diseases on account of direct pulmonary delivery, good drug stability and satisfactory patient compliance. However, an indistinct understanding of pulmonary delivery processes (PDPs) hindered the development of DPIs. Most current evaluation methods explored the PDPs with over-simplified models, leading to uncompleted investigations of the whole or partial PDPs. In the present research, an innovative modular process analysis platform (MPAP) was applied to investigate the detailed mechanisms of each PDP of DPIs with different carrier particle sizes (CPS). The MPAP was composed of a laser particle size analyzer, an inhaler device, an artificial throat and a pre-separator, to investigate the fluidization and dispersion, transportation, detachment and deposition process of DPIs. The release profiles of drug, drug aggregation and carrier were monitored in real-time. The influence of CPS on PDPs and corresponding mechanisms were explored. The powder properties of the carriers were investigated by the optical profiler and Freeman Technology four powder rheometer. The next generation impactor was employed to explore the aerosolization performance of DPIs. The novel MPAP was successfully applied in exploring the comprehensive mechanism of PDPs, which had enormous potential to be used to investigate and develop DPIs.

Methods:Two thousand standard sections images werre collected from 2 000 clinical retrospective pediatric hip ultrasound videos from January 2019 to January 2021. All standard sections were annotated by the annotation team through the self-designed software based on Python 3.6 environment for image cross-media data annotation and manual review standardization process with unified standards. Among them, 1 753 were randomly selected for training the deep learning system, and the remaining 247 were used for testing the system. Further, 200 standard sections were randomly selected from the test set, and 8 clinicians independently completed the film reading annotation. The 8 independent results were then compared with the AI results.Results:The testing set consists of 247 patients. Compared with the clinician's measurements, the area under the receiver operating characteristic curve (AUC) of diagnosing hip joint maturity was 0.865, the sensitivity was 76.19%, and the specificity was 96.9%. The AUC of AI system interpretation under Graf detailed typing was 0.575, the sensitivity was 25.90%, the specificity was 89.10%. The 95% LoA of α-angle determined by Bland-Altman method, of -4.7051° to 6.5948° ( Bias -0.94, P<0.001), compared with clinicians' measurements. The 95% LoA of β-angle, of -7.7191 to 6.8777 ( Bias -0.42, P=0.077). Compared with those from 8 clinicians, the results of AI system interpretation were more stable, and the β-angle effect was more prominent. Conclusion:The AI system can quickly and accurately measure the Graf correlation index of standard DDH ultrasonic standard diagnosis plane.

Proteins and peptides have become a significant therapeutic modality for various diseases because of their high potency and specificity. However, the inherent properties of these drugs, such as large molecular weight, poor stability, and conformational flexibility, make them difficult to be formulated and delivered. Injection is the primary route for clinical administration of protein and peptide drugs, which usually leads to poor patient's compliance. As a portable, minimally invasive device, microneedles (MNs) can overcome the skin barrier and generate reversible microchannels for effective macromolecule permeation. In this review, we highlighted the recent advances in MNs-mediated transdermal delivery of protein and peptide drugs. Emphasis was given to the latest development in representative MNs design and fabrication. We also summarize the current application status of MNs-mediated transdermal protein and peptide delivery, especially in the field of infectious disease, diabetes, cancer, and other disease therapy. Finally, the current status of clinical translation and a perspective on future development are also provided.

Membrane-disruptive peptides/peptidomimetics (MDPs) are antimicrobials or anticarcinogens that present a general killing mechanism through the physical disruption of cell membranes, in contrast to conventional chemotherapeutic drugs, which act on precise targets such as DNA or specific enzymes. Owing to their rapid action, broad-spectrum activity, and mechanisms of action that potentially hinder the development of resistance, MDPs have been increasingly considered as future therapeutics in the drug-resistant era. Recently, growing experimental evidence has demonstrated that MDPs can also be utilized as adjuvants to enhance the therapeutic effects of other agents. In this review, we evaluate the literature around the broad-spectrum antimicrobial properties and anticancer activity of MDPs, and summarize the current development and mechanisms of MDPs alone or in combination with other agents. Notably, this review highlights recent advances in the design of various MDP-based drug delivery systems that can improve the therapeutic effect of MDPs, minimize side effects, and promote the co-delivery of multiple chemotherapeutics, for more efficient antimicrobial and anticancer therapy.

Psoriatic arthritis (PsA) is a complicated psoriasis comorbidity with manifestations of psoriatic skin and arthritic joints, and tailoring specific treatment strategies for simultaneously delivering different drugs to different action sites in PsA remains challenging. We developed a need-based layered dissolving microneedle (MN) system loading immunosuppressant tacrolimus (TAC) and anti-inflammatory diclofenac (DIC) in different layers of MNs,