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Abstract Background The incidence of lung cancer combined with pulmonary tuberculosis has been increasing, but there is relatively limited published literature on the topic of lung cancer combined with tuberculosis (LC‐PTB) from a bibliometric perspective. Therefore, in this study, we aimed to quantitatively analyze the LC‐PTB‐related literature to better understand the current status of this field and identify future research trends. Methods We searched for articles related to LC‐PTB using the Web of Science Core Collection (SCI‐E) and conducted a visual analysis of publication quantity, countries, institutions, authors, journals, references, and keywords using bibliometric software (CiteSpace, VOSviewer, and Scimago Graphica). Results As of January 8, 2024, a total of 460 publications related to LC‐PTB were included for analysis from 3705 retrieved records. The number of publications has been increasing almost yearly, with most from China (n = 123), followed by the United States (n = 77). Taipei Medical University contributed the most publications (n = 11). Jing‐Yang Huang and Yung‐Po Liaw (eight documents each) ranked first among the included authors. The Journal of Thoracic Oncology was the most productive academic journal on LC‐PTB. The aggregation of key nodes in the co‐citation network and the chronological sequence indicated that LC‐PTB research has shifted from initial hotspots such as lung diseases, bronchitis, and exposure to recent areas, including immunotherapy, immune checkpoint inhibitors, and nivolumab. Conclusion In this study, we visualized the current status of LC‐PTB research as well as future trends using bibliometric methods, providing new insights into the differential diagnosis of LC‐PTB and its related promoting mechanisms.

Abstract Tuberculosis (TB) is an infectious disease that significantly threatens human health. However, the differential diagnosis of latent tuberculosis infection (LTBI) and active tuberculosis (ATB) remains a challenge for clinicians in early detection and preventive intervention. In this study, we developed a novel biomarker named HP16118P, utilizing 16 helper T lymphocyte (HTL) epitopes, 11 cytotoxic T lymphocyte (CTL) epitopes, and 8 B cell epitopes identified from 15 antigens associated with LTBI-RD using the IEDB database. We analyzed the physicochemical properties, spatial structure, and immunological characteristics of HP16118P using various tools, which indicated that it is a hydrophilic and relatively stable alkaline protein. Furthermore, HP16118P exhibited good antigenicity and immunogenicity, while being non-toxic and non-allergenic, with the potential to induce immune responses. We observed that HP16118P can stimulate the production of high levels of IFN-γ+ T lymphocytes in individuals with ATB, LTBI, and health controls. IL-5 induced by HP16118P demonstrated potential in distinguishing LTBI individuals and ATB patients (p=0.0372, AUC=0.8214, 95% CI [0.5843 to 1.000]) with a sensitivity of 100% and specificity of 71.43%. Furthermore, we incorporated the GM-CSF, IL-23, IL-5, and MCP-3 induced by HP16118P into 15 machine learning algorithms to construct a model. It was found that the Quadratic discriminant analysis model exhibited the best diagnostic performance for discriminating between LTBI and ATB, with a sensitivity of 1.00, specificity of 0.86, and accuracy of 0.93. In summary, HP16118P has demonstrated strong antigenicity and immunogenicity, with the induction of GM-CSF, IL-23, IL-5, and MCP-3, suggesting their potential for the differential diagnosis of LTBI and ATB.

Fatigue damage can lead to failures of structural systems. To reduce the failure risk and enhance the reliability of structural systems, inspection and maintenance interventions are required, and it is important to develop an efficient inspection strategy. This study, for the first time, develops a system-level reliability growth model to establish efficient inspection planning. System-level reliability growth is defined as an increase in the percentage of the system reliability index with and without inspection. The probabilistic S-N approach is used to obtain the reliability index without inspection. Moreover, advanced risk analysis and Bayesian inference techniques are used to obtain the reliability index with inspection. The optimal inspection planning is obtained by maximizing system-level reliability growth. This model is applied to an offshore wind farm. The results show that inspection efficiency can be improved by increasing the number of repair objects in response to a ‘detection’ inspection outcome, changing the inspection object for each inspection, and increasing the inspection quality. The maximum system-level reliability growth gained from one additional inspection decreases as the number of inspections increases. This study quantifies the inspection efficiency of offshore wind farms by explicit system-level reliability growth computation, offering valuable insights for promoting sustainable energy solutions.

Abstract Although tuberculosis (TB) is an infectious disease, the progression of the disease following Mycobacterium tuberculosis (MTB) infection is closely associated with the host's immune response. In this review, a comprehensive analysis of TB prevention, diagnosis, and treatment was conducted from an immunological perspective. First, we delved into the host's immune response mechanisms against MTB infection as well as the immune evasion mechanisms of the bacteria. Addressing the challenges currently faced in TB diagnosis and treatment, we also emphasized the importance of protein, genetic, and immunological biomarkers, aiming to provide new insights for early and personalized diagnosis and treatment of TB. Building upon this foundation, we further discussed intervention strategies involving chemical and immunological treatments for the increasingly critical issue of drug‐resistant TB and other forms of TB. Finally, we summarized TB prevention, diagnosis, and treatment challenges and put forward future perspectives. Overall, these findings provide valuable insights into the immunological aspects of TB and offer new directions toward achieving the WHO's goal of eradicating TB by 2035.

Summary: Lung cancer (LC) and tuberculosis (TB) are two major global public health problems, and the incidence of LC-TB is currently on the rise. Therefore effective clinical interventions are crucial for LC-TB. The aim of this review is to provide up-to-date information on the immunological profile and therapeutic biomarkers in patients with LC-TB. We discuss the immune mechanisms involved, including the immune checkpoints that play an important role in the treatment of patients with LC-TB. In addition, we explore the susceptibility of patients with LC to TB and summarise the latest research on LC-TB. Finally, we discuss future prospects in this field, including the identification of potential targets for immune intervention. In conclusion, this review provides important insights into the complex relationship between LC and TB and highlights new advances in the detection and treatment of both diseases.

Tuberculosis (TB), caused by Mycobacterium tuberculosis (MTB), is a prevalent global infectious disease and a leading cause of mortality worldwide. Currently, the only available vaccine for TB prevention is Bacillus Calmette–Guérin (BCG). However, BCG demonstrates limited efficacy, particularly in adults. Efforts to develop effective TB vaccines have been ongoing for nearly a century. In this review, we have examined the current obstacles in TB vaccine research and emphasized the significance of understanding the interaction mechanism between MTB and hosts in order to provide new avenues for research and establish a solid foundation for the development of novel vaccines. We have also assessed various TB vaccine candidates, including inactivated vaccines, attenuated live vaccines, subunit vaccines, viral vector vaccines, DNA vaccines, and the emerging mRNA vaccines as well as virus-like particle (VLP)-based vaccines, which are currently in preclinical stages or clinical trials. Furthermore, we have discussed the challenges and opportunities associated with developing different types of TB vaccines and outlined future directions for TB vaccine research, aiming to expedite the development of effective vaccines. This comprehensive review offers a summary of the progress made in the field of novel TB vaccines.

In order to realize the automatic detection of surface defects of lithium battery pole piece, a method for detection and identification of surface defects of lithium battery pole piece based on multi-feature fusion and PSO-SVM was proposed in this paper. Firstly, image subtraction and contrast adjustment were used to preprocess the defect image to weaken the influence of non-defective areas and enhance the defect features. Then, Canny algorithm and the AND logical operation were used to extract the image of defect area. Next, the texture feature, edge feature, and HOG feature were combined to extract the feature of the defect area image. Finally, the support vector machine (SVM) optimized by particle swarm optimization (PSO) was used to automatically identify and classify defect images. The experimental results show that the proposed method in this paper can effectively detect surface multiple types defects of lithium battery pole piece, and the average recognition rate of defects reaches 98.3%, which is an effective and feasible automatic defect detection and identification method.

At present， the operate time of the most manipulators are not optimally during the working process. In order to improve the working efficiency of the manipulator， a time optimal manipulator trajectory planning method based on the complex method is proposed. The method of 5-7-5 polynomial interpolation method is taken as the basis of trajectory planning， the motion angle， motion velocity and motion acceleration are taken as constraint， the optimization is carried out by using the complex method， the time optimal motion trajectory that meets the motion requirements is obtained. The FS20N robot is used as the object for research and analysis， and imported into MATLAB for verification， the simulation structure shows that the complex method effectively optimizes the time.

Online tracking is a challenging task to develop effective and efficient models to account for appearance change. However, most tracking algorithms only consider the holistic or local information and do not make full use of the appearance information. In this study, a novel tracking algorithm with sparse representation is proposed and the online classifier is learned to discriminate the target from the background. To reduce visual drift problem which is encountered in object tracking, a two‐stage sparse representation method is proposed. The holistic information is used to estimate the initial tracking position, and the local information is used to determine the final tracking position. To improve the performance of the classifier and robustness of the algorithm, the kernel function is applied on the sparse representation. Moreover, the dimension of the target is reduced via compressive sensing. Besides, a simple and effective method for dictionary update is proposed. Both qualitative and quantitative evaluations on challenging image sequences demonstrate that the proposed algorithm performs favourably against several state‐of‐the‐art algorithms.

AimsA strategy based on therapeutic drug monitoring and population pharmacokinetic (popPK) models would likely increase the rate of clinical remission (CR) after infliximab (IFX) induction in patients with Crohn’s disease (CD). This study aimed to evaluate the relationship between early IFX levels and antibodies to infliximab (ATI) and CR at week 14 and simulate the probability of attaining the identified exposure target.MethodsPatients with CD (n = 140) treated with IFX were enrolled to develop the popPK model. Of these, 43 moderate-to-severe patients with CD were followed up at week 14. Simulations were performed on patients with different dosage regimens and covariates.ResultsIFX levels >20.08 μg/mL at week 2, >18.44 μg/mL at week 6, and >3.08 μg/mL at week 14 were linked to CR. A one-compartment model fit the data best. The covariates influencing clearance were fat free mass, albumin and ATI levels. To achieve IFX levels >20.08 μg/mL at week 2, ≥400 mg IFX was predicted to be required in over 50% patients with 45–70 kg and 35–45 g/L albumin, except for patients with 70 kg and 30 g/L albumin.ConclusionIFX levels >20.08 μg/mL at week 2 and absence of ATI at week 14 are associated with CR. Optimising IFX induction dosing will be critical to achieve the target of early IFX levels associated with CR.

Alzheimer's disease (AD) is the leading cause of dementia and is rapidly becoming one of the most costly, fatal diseases, which is typically discovered in the late stage of molecular pathology, at which point medication intervention is irreversible. As a result, there is an urgent need for a low-cost, least-invasive way of screening cognitive impairment, with the goal of identifying persons at risk of AD. Mild cognitive impairment (MCI) has been described as a transitional state between normal cognitive aging and AD. Early detection and timely tracking of MCI can to some extent prevent the progression towards AD. We found a population in Northwestern China has a comparatively high prevalence of MCI. Continued education, consistent exercise, and a secure financial situation can all help older people maintain cognitive function. Due to the critical role of circulating microRNAs in intercellular signaling and the perturbations thereof, their investigation has assumed paramount significance in elucidating various pathological conditions. Numerous investigations have substantiated the significance of circulating miRNAs specifically in MCI. Here, we evaluated miR-483-5p (Area Under the Curve (AUC) is 0.901, sensitivity 79.2 % and specificity 100 %) and miR-502-5p (AUC is 0.872, sensitivity 79.2 % and specificity 83.3 %), which were derived from plasma exosomes and maintained at high levels in elderly people with MCI, could be employed as promising noninvasive biomarkers.

The classification of surrounding rock is crucial for formulating safe tunnel construction plans and support measures. However, the complex geological environment of tunnels presents a challenge in obtaining accurate drilling parameters for rock mass classification. This paper presents the development of a rock drilling testing system, which includes a propulsion speed acquisition system, oil pressure acquisition system, air pressure acquisition system, and an automatic data acquisition system. This system enables real-time, high-precision automatic collection and storage of parameters such as propulsion speed, with data collected twice per second for each parameter. Leveraging the Qingdao Metro Line 6 as a case study, we conducted rock mass drilling and constructed a rock mass classification database. By employing kernel density estimation and Pearson correlation analysis, we quantified the correlation between rock mass classification and the drilling parameters. The results indicated that relying on a single drilling parameter is insufficient for accurately determining rock mass classification. Both impact pressure and rotational pressure showed the strongest correlation with rock mass classification, each with a correlation coefficient below −0.8 (indicating a strong negative correlation). Outlier values of drilling parameters were excluded using the interval method. Based on the remaining data, we established an intelligent rock mass classification model using the random forest algorithm. This model demonstrated good accuracy and generalization performance, with an average accuracy exceeding 0.9. The proposed rock drilling testing system, combined with the intelligent rock mass classification model, forms an integrated system for the intelligent identification of rock mass grades. This system has significant implications for the intelligent and safe construction of drill-and-blast tunnels.

This investigation explores the structural and electronic properties of low-temperature-grown (InAs)4(GaAs)3/Be-doped InAlAs and InGaAs/Be-doped InAlAs multiple quantum wells (MQWs), utilizing migration-enhanced epitaxy (MEE) and conventional molecular beam epitaxy (MBE) growth mode. Through comprehensive characterization methods including transmission electron microscopy (TEM), Raman spectroscopy, atomic force microscopy (AFM), pump–probe transient reflectivity, and Hall effect measurements, the study reveals significant distinctions between the two types of MQWs. The (InAs)4(GaAs)3/Be-doped InAlAs MQWs grown via the MEE mode exhibit enhanced periodicity and interface quality over the InGaAs/Be-InAlAs MQWs grown through the conventional molecule beam epitaxy (MBE) mode, as evidenced by TEM. The AFM results indicate lower surface roughness for the (InAs)4(GaAs)3/Be-doped InAlAs MQWs by using the MEE mode. Raman spectroscopy reveals weaker disorder-activated modes in the (InAs)4(GaAs)3/Be-doped InAlAs MQWs by using the MEE mode. This originates from utilizing the (InAs)4(GaAs)3 short period superlattices rather than InGaAs, which suppresses the arbitrary distribution of Ga and In atoms during the InGaAs growth. Furthermore, pump–probe transient reflectivity measurements show shorter carrier lifetimes in the (InAs)4(GaAs)3/Be-doped InAlAs MQWs, attributed to a higher density of antisite defects. It is noteworthy that room temperature Hall measurements imply that the mobility of (InAs)4(GaAs)3/Be-doped InAlAs MQWs grown at a low temperature of 250 °C via the MEE mode is superior to that of InGaAs/Be-doped InAlAs MQWs grown in the conventional MBE growth mode, reaching 2230 cm2/V.s. The reason for the higher mobility of (InAs)4(GaAs)3/Be-doped InAlAs MQWs is that this short-period superlattice structure can effectively suppress alloy scattering caused by the arbitrary distribution of In and Ga atoms during the growth process of the InGaAs ternary alloy. These results exhibit the promise of the MEE growth approach for growing high-performance MQWs for advanced optoelectronic applications, notably for high-speed optoelectronic devices like THz photoconductive antennas.

The growth of InGaAs quantum wells (QWs) epitaxially on InP substrates is of great interest due to their wide application in optoelectronic devices. However, conventional molecular beam epitaxy requires substrate temperatures between 400 and 500 °C, which can lead to disorder scattering, dopant diffusion, and interface roughening, adversely affecting device performance. Lower growth temperatures enable the fabrication of high-speed optoelectronic devices by increasing arsenic antisite defects and reducing carrier lifetimes. This work investigates the low-temperature epitaxial growth of InAs/GaAs short-period superlattices as an ordered replacement for InGaAs quantum wells, using migration-enhanced epitaxy (MEE) with low growth temperatures down to 200–250 °C. The InAs/GaAs multi-quantum wells with InAlAs barriers using MEE grown at 230 °C show good single crystals with sharp interfaces, without mismatch dislocations found. The Raman results reveal that the MEE mode enables the growth of (InAs)4(GaAs)3/InAlAs QWs with excellent periodicity, effectively reducing alloy scattering. The room temperature (RT) photoluminescence (PL) measurement shows the strong PL responses with narrow peaks, revealing the good quality of the MEE-grown QWs. The RT electron mobility of the sample grown in low-temperature MEE mode is as high as 2100 cm2/V∗s. In addition, the photoexcited band-edge carrier lifetime was about 3.3 ps at RT. The high-quality superlattices obtained confirm MEE’s effectiveness for enabling advanced III-V device structures at reduced temperatures. This promises improved performance for applications in areas such as high-speed transistors, terahertz imaging, and optical communications.

The low-temperature-grown InGaAs (LT-InGaAs) photoconductive antenna has received great attention for the development of highly compact and integrated cheap THz sources. However, the performance of the LT-InGaAs photoconductive antenna is limited by its low resistivity and mobility. The generated radiated power is much weaker compared to the low-temperature-grown GaAs-based photoconductive antennas. This is mainly caused by the low abundance of excess As in LT-InGaAs with the conventional growth mode, which inevitably gives rise to the formation of As precipitate and alloy scattering after annealing. In this paper, the migration-enhanced molecular beam epitaxy technique is developed to grow high-quality (InAs)m/(GaAs)n short-period superlattices with a sharp interface instead of InGaAs on InP substrate. The improved electron mobility and resistivity at room temperature (RT) are found to be 843 cm2/(V·s) and 1648 ohm/sq, respectively, for the (InAs)m/(GaAs)n short-period superlattice. The band-edge photo-excited carrier lifetime is determined to be ~1.2 ps at RT. The calculated photocurrent intensity, obtained by solving the Maxwell wave equation and the coupled drift–diffusion/Poisson equation using the finite element method, is in good agreement with previously reported results. This work may provide a new approach for the material growth towards high-performance THz photoconductive antennas with high radiation power.