Rapid Detection of SARS-CoV-2 Variants Using an Angiotensin-Converting Enzyme 2-Based Surface-Enhanced Raman Spectroscopy Sensor Enhanced by CoVari Deep Learning Algorithms.

An integrated approach combining surface-enhanced Raman spectroscopy (SERS) with a specialized deep learning algorithm to rapidly and accurately detect and quantify SARS-CoV-2 variants is developed based on an angiotensin-converting enzyme 2 (ACE2)-functionalized AgNR@SiO2 array SERS sensor. SERS spectra with concentrations of different variants were collected using a portable Raman system. After appropriate spectral preprocessing, a deep learning algorithm, CoVari, is developed to predict both the viral variant species and concentrations. Using a 10-fold cross-validation strategy, the model achieves an average accuracy of 99.9% in discriminating between different virus variants and R2 values larger than 0.98 for quantifying viral concentrations of the three viruses, demonstrating the high quality of the detection. The limit of detection of the ACE2 SERS sensor is determined to be 10.472, 11.882, and 21.591 PFU/mL for SARS-CoV-2, SARS-CoV-2 B1, and CoV-NL63, respectively. The feature importance of virus classification and concentration regression in the CoVari algorithm are calculated based on a permutation algorithm, which showed a clear correlation to the biochemical origins of the spectra or spectral changes. In an unknown specimen test, classification accuracy can achieve >90% for concentrations larger than 781 PFU/mL, and the predicted concentrations consistently align with actual values, highlighting the robustness of the proposed algorithm. Based on the CoVari architecture and the output vector, this algorithm can be generalized to predict both viral variant species and concentrations simultaneously for a broader range of viruses. These results demonstrate that the SERS + CoVari strategy has the potential for rapid and quantitative detection of virus variants and potentially point-of-care diagnostic platforms.

Precision classification and quantitative analysis of bacteria biomarkers via surface-enhanced Raman spectroscopy and machine learning.

The SERS spectra of six bacterial biomarkers, 2,3-DHBA, 2,5-DHBA, Pyocyanin, lipoteichoic acid (LTA), Enterobactin, and ß-carotene, of various concentrations, were obtained from silver nanorod array substrates, and the spectral peaks and the corresponding vibrational modes were identified to classify different spectra. The spectral variations in three different concentration regions due to various reasons have imposed a challenge to use classic calibration curve methods to quantify the concentration of biomarkers. Depending on baseline removal strategy, i.e., local or global baseline removal, the calibration curve differed significantly. With the aid of convolutional neural network (CNN), a two-step process was established to classify and quantify biomarker solutions based on SERS spectra: using a specific CNN model, a remarkable differentiation and classification accuracy of 99.99 % for all six biomarkers regardless of the concentration can be achieved. After classification, six regression CNN models were established to predict the concentration of biomarkers, with coefficient of determination R2 > 0.97 and mean absolute error (MAE) < 0.27. The feature of important calculations indicates the high classification and quantification accuracies were due to the intrinsic spectral features in SERS spectra. This study showcases the synergistic potential of SERS and advanced machine learning algorithms and holds significant promise for bacterial infection diagnostics.

Directional regulation of reactive oxygen species in titanium dioxide boosting the photocatalytic degradation performance of azo dyes.

It has been widely accepted that the generation of reactive oxygen species such as superoxide radical, hydroxyl radical, and hydrogen peroxide during photocatalysis is responsible for the degradation of azo dyes. However, it is unclear which reactive oxygen species primarily contributes to the degradation efficiency of azo dyes. Here, we demonstrate that the directional regulation of reactive oxygen species in titanium dioxide (TiO2) to form superoxide radicals by ethylenediaminetetraacetic acid disodium salt (EDTA-2Na) can significantly improve the degradation performance of methyl orange. The optimized addition of EDTA-2Na can completely degrade azo dyes such as methyl orange, acid orange and alkaline orange at a concentration of 10 mg/L in about 20 min, which is not only higher than that achieved by pristine TiO2 under Xe lamp light but also far superior to the reported degradation efficiency of modified TiO2. Even under natural sunlight, this strategy can also effectively decompose azo dyes, demonstrating the great potential for practical water treatment using low-cost TiO2 photocatalysts.

Comparative Analysis of mRNA and lncRNA Expression Profiles in Testicular Tissue of Sexually Immature and Sexually Mature Mongolian Horses.

Testicular development and spermatogenesis are tightly regulated by both coding and non-coding genes, with mRNA and lncRNA playing crucial roles in post-transcriptional gene expression regulation. However, there are significant differences in regulatory mechanisms before and after sexual maturity. Nevertheless, the mRNAs and lncRNAs in the testes of Mongolian horses have not been systematically identified. In this study, we first identified the testicular tissues of sexually immature and sexually mature Mongolian horses at the tissue and protein levels, and comprehensively analyzed the expression profiles of mRNA and lncRNA in the testes of 1-year-old (12 months, n = 3) and 10-year-old (n = 3) Mongolian horses using RNA sequencing technology. Through gene expression analysis, we identified 16,582 mRNAs and 2128 unknown lncRNAs that are commonly expressed in both sexually immature and sexually mature Mongolian horses. Meanwhile, 9217 mRNAs (p < 0.05) and 2191 unknown lncRNAs (p < 0.05) were identified as differentially expressed between the two stages, which were further validated by real-time fluorescent quantitative PCR and analyzed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). The analysis results showed that genes in the sexually immature stage were mainly enriched in terms related to cellular infrastructure, while genes in the sexually mature stage were enriched in terms associated with hormones, metabolism, and spermatogenesis. In summary, the findings of this study provide valuable resources for a deeper understanding of the molecular mechanisms underlying testicular development and spermatogenesis in Mongolian horses and offer new perspectives for future related research.

Leucine promotes energy metabolism and stimulates slow-twitch muscle fibers expression through AMPK/mTOR signaling in equine skeletal muscle satellite cells.

Previous research has shown that leucine (Leu) can stimulate and enhance the proliferation of equine skeletal muscle satellite cells (SCs). The gene expression profile associated with Leu-induced proliferation of equine SCs has also been documented. However, the specific role of Leu in regulating the expression of slow-twitch muscle fibers (slow-MyHC) and mitochondrial function in equine SCs, as well as the underlying mechanism, remains unclear. During this investigation, equine SCs underwent culturing in differentiation medium and were subjected to varying concentrations of Leu (0 mM, 0.5 mM, 1 mM, 2 mM, 5 mM, and 10 mM) over a span of 3 days. AMP-activated protein kinase (AMPK) inhibitor Compound C and mammalian target of rapamycin complex (mTOR) inhibitor Rapamycin were utilized to explore its underlying mechanism. Here we showed that the expression of slow-MyHC at 2 mM Leu level was significantly higher than the concentration levels of 0 mM,0.5 mM and 10 mM (P <0.01), and there was no significant difference compared to other groups (P > 0.05); the basal respiration, maximum respiration, standby respiration and the expression of slow-MyHC, PGC-1α, Cytc, ND1, TFAM, and COX1 were significantly increased with Leu supplementation (P < 0.01). We also found that Leu up-regulated the expression of key proteins on AMPK and mTOR signaling pathways, including LKB1, p-LKB1, AMPK, p-AMPK, S6, p-S6, 4EBP1, p-4EBP1, mTOR and p-mTOR (P < 0.05 or P < 0.01). Notably, when we treated the equine SCs with the AMPK inhibitor Compound C and the mTOR inhibitor Rapamycin, we observed a reduction in the beneficial effects of Leu on the expression of genes related to slow-MyHC and signaling pathway-related gene expressions. This study provides novel evidence that Leu promotes slow-MyHC expression and enhances mitochondrial function in equine SCs through the AMPK/mTOR signaling pathways, shedding light on the underlying mechanisms involved in these processes for the first time.

Unlocking Predictive Capability and Enhancing Sensing Performances of Plasmonic Hydrogen Sensors via Phase Space Reconstruction and Convolutional Neural Networks.

This study innovates plasmonic hydrogen sensors (PHSs) by applying phase space reconstruction (PSR) and convolutional neural networks (CNNs), overcoming previous predictive and sensing limitations. Utilizing a low-cost and efficient colloidal lithography technique, palladium nanocap arrays are created and their spectral signals are transformed into images using PSR and then trained using CNNs for predicting the hydrogen level. The model achieves accurate predictions with average accuracies of 0.95 for pure hydrogen and 0.97 for mixed gases. Performance improvements observed are a reduction in response time by up to 3.7 times (average 2.1 times) across pressures, SNR increased by up to 9.3 times (average 3.9 times) across pressures, and LOD decreased from 16 Pa to an extrapolated 3 Pa, a 5.3-fold improvement. A practical application of remote hydrogen sensing without electronics in hydrogen environments is actualized and achieves a 0.98 average test accuracy. This methodology reimagines PHS capabilities, facilitating advancements in hydrogen monitoring technologies and intelligent spectrum-based sensing.

PDIA2 has a dual function in promoting androgen deprivation therapy induced venous thrombosis events and castrate resistant prostate cancer progression.

Androgen deprivation therapy (ADT) is the first line of treatment for metastatic prostate cancer (PCa) that effectively delays the tumor progression. However, it also increases the risk of venous thrombosis event (VTE) in patients, a leading cause of mortality. How a pro-thrombotic cascade is induced by ADT remains poorly understood. Here, we report that protein disulfide isomerase A2 (PDIA2) is upregulated in PCa cells to promote VTE formation and enhance PCa cells resistant to ADT. Using various in vitro and in vivo models, we demonstrated a dual function of PDIA2 that enhances tumor-mediated pro-coagulation activity via tumor-derived extracellular vehicles (EVs). It also stimulates PCa cell proliferation, colony formation, and xenograft growth androgen-independently. Mechanistically, PDIA2 activates the tissue factor (TF) on EVs through its isomerase activity, which subsequently triggers a pro-thrombotic cascade in the blood. Additionally, TF-containing EVs can activate the Src kinase inside PCa cells to enhance the AR signaling ligand independently. Androgen deprivation does not alter PDIA2 expression in PCa cells but enhances PDIA2 translocation to the cell membrane and EVs via suppressing the clathrin-dependent endocytic process. Co-recruitment of AR and FOXA1 to the PDIA2 promoter is required for PDIA2 transcription under androgen-deprived conditions. Importantly, blocking PDIA2 isomerase activity suppresses the pro-coagulation activity of patient plasma, PCa cell, and xenograft samples as well as castrate-resistant PCa xenograft growth. These results demonstrate that PDIA2 promotes VTE and tumor progression via activating TF from tumor-derived EVs. They rationalize pharmacological inhibition of PDIA2 to suppress ADT-induced VTE and castrate-resistant tumor progression.

Suppression of cracking in drying colloidal suspensions with chain-like particles.

The prevention of drying-induced cracking is crucial in maintaining the mechanical integrity and functionality of colloidal deposits and coatings. Despite exploring various approaches, controlling drying-induced cracking remains a subject of great scientific interest and practical importance. By introducing chain-like particles composed of the same material and with comparable size into commonly used colloidal suspensions of spherical silica nanoparticles, we can significantly reduce the cracks formed in dried particle deposits and achieve a fivefold increase in the critical cracking thickness of colloidal silica coatings. The mechanism underlying the crack suppression is attributed to the increased porosity and pore sizes in dried particle deposits containing chain-like particle, which essentially leads to reduction in internal stresses developed during the drying process. Meanwhile, the nanoindentation measurements reveal that colloidal deposits with chain-like particles exhibit a smaller reduction in hardness compared to those reported using other cracking suppression approaches. This work demonstrates a promising technique for preparing colloidal coatings with enhanced crack resistance while maintaining desirable mechanical properties.

Investigating acupoint selection and combinations of acupuncture for primary idiopathic tinnitus using data mining.

BACKGROUND: Acupuncture is widely used in the treatment of tinnitus worldwide because of its good efficacy and safety. However, the criteria for selecting acupoint prescriptions and combinations have not been summarized. Therefore, data mining was used herein to determine the treatment principles and the most effective acupoint selection for the treatment of idiopathic tinnitus. METHODS: The clinical research literature of acupuncture in the treatment of idiopathic tinnitus from the establishment of the database to September 1, 2023 in China National Knowledge Infrastructure, China Medical Journal Full-text Database, PubMed, Embase, Cochrane Library and Web of Science databases was retrieved and extracted. Microsoft Excel 2016 was used to establish the acupoint prescription database and the frequency statistics of acupoints, meridians and specific acupoints were carried out. IBM SPSS Statistics 25.0 software was used for cluster analysis of acupoints, and IBM SPSS Modeler18.0 software was used for association rule analysis of acupoints. RESULTS: A total of 112 articles were included, involving 221 acupuncture prescriptions, including 99 acupoints, with a total frequency of 1786 times. The 5 most frequently used acupoints were Tinggong (SI19), Tinghui (GB2), Yifeng (TE17), Ermen (TE21), and Zhongzhu (TE3). The commonly used meridians were Sanjiao meridian of hand-shaoyang, Gallbladder meridian of foot-shaoyang and Small intestine meridian of hand-taiyang. The specific points are mostly Crossing point, Five-shu point and Yuan-primary point. The core acupoint combination of association rules was Ermen (TE21)-Tinggong (SI19)-Tinghui (GB2)-Yifeng (TE17), and 3 effective clustering groups were obtained by cluster analysis of high-frequency acupoints. CONCLUSION: In this study, the published literature on acupuncture treatment of idiopathic tinnitus was analyzed by data mining, and the relationship between acupoints was explored, which provided a more wise choice for clinical acupuncture treatment of idiopathic tinnitus.

The N-Oscillator Born-Kuhn Model: An In-Depth Analysis of Chiro-Optical Properties in Complex Chiral Systems.

A comprehensive theory is developed for the chiral optical response of two configurations of the N-oscillator Born-Kuhn model (NOBK): the helically stacked and the corner stacked models. In the helical NOBK model, there is always a chiral response regardless of the value of N, whereas in the corner NOBK, only configurations with even N demonstrate a chiral response. Generally, the magnitudes of optical rotatory dispersion (ORD) and circular dichroism (CD) increase with N when the parameters of each oscillator are fixed. In cases of weak coupling, the spectral shapes of ORD and CD remain invariant, while strong coupling significantly alters the spectral shapes. For large damping, the spectral amplitude becomes smaller, and the spectral features become broader. In the presence of small damping, strong coupling introduces degeneracy in the coupled oscillator system, leading to multiple spectral features in both ORD and CD across the entire spectral region. This simple model can not only help in the design of tunable chiral metamaterials but also enhance our understanding of chiro-optical responses in structures with different configurations.

Dynamic Clustering and Scaling Behavior of Active Particles under Confinement.

A systematic investigation of the dynamic clustering behavior of active particles under confinement, including the effects of both particle density and active driving force, is presented based on a hybrid coarse-grained molecular dynamics simulation. First, a series of scaling laws are derived with power relationships for the dynamic clustering time as a function of both particle density and active driving force. Notably, the average number of clusters N¯ assembled from active particles in the simulation system exhibits a scaling relationship with clustering time t described by N¯âˆt-m. Simultaneously, the scaling behavior of the average cluster size S¯ is characterized by S¯âˆtm. Our findings reveal the presence of up to four distinct dynamic regions concerning clustering over time, with transitions contingent upon the particle density within the system. Furthermore, as the active driving force increases, the aggregation behavior also accelerates, while an increase in density of active particles induces alterations in the dynamic procession of the system.

Bidirectional-Reinforced Carbon Fiber/Polyether-Ether-Ketone Composite Thin-Walled Pipes via Pultrusion-Winding for On-Orbit Additive Manufacturing.

Benefitting from lightweight, high strength, long life, and green recyclability, continuous fiber reinforced thermoplastic composite (CFTPC) pipes have attracted extensive interest, especially in the on-orbit additive manufacturing of structural components. However, the preparation of CFTPC pipes remains challenging due to the on-orbit limited space and high processing temperature of thermoplastic resin. Here, we report an effective approach for high performance carbon fiber/polyether-ether-ketone (CF/PEEK) thin-walled pipes via bidirectional reinforcement using the pultrusion-winding technique. The continuous fabrication of thin-walled pipes can be achieved, but the limitation by the size of core mold is also broken. The compressive and shear performance of CF/PEEK pipes with different layer designs have been studied based on experiments and simulations. With the increase in axial prepreg tape layer, the resultant CF/PEEK pipes exhibit greatly improved axial compression strength. The finite element analysis indicates that the maximum axial stress is decreased due to the axial enhancement. The flexural strength is greatly proved with pultrusion-winding cycles. The simulation confirms that the circumferential strain is effectively reduced. The high performance of bidirectional reinforced CF/PEEK pipes and the facile controllability of this approach highlight their suitability for utilization in on-orbit manufacturing of large-scale structures.

Drug-Coated Balloon Angioplasty for Dysfunctional Arteriovenous Hemodialysis Fistulae: A Randomized Controlled Trial.

BACKGROUND: The aim of this study was to evaluate the efficacy and safety of paclitaxel-coated balloons (AcoArt Orchid) in treating dysfunctional arteriovenous fistulae. METHODS: The drug-eluting balloon for arteriovenous (AV) fistula in China trial was a prospective, multicenter, randomized controlled study. Patients who had ≥50% venous stenosis of the AV fistula and symptoms indicating significant hemodynamic changes were included. After successful predilation with a high-pressure balloon (residual stenosis ≤30%), patients were randomized 1:1 to either a paclitaxel-coated balloon or an uncoated control balloon. The primary efficacy outcome was assessed at 6 months, and safety assessment was conducted within 30 days of the procedure. The 12-month results were also analyzed. RESULTS: The study included 244 patients, equally distributed between the two groups. The primary target lesion patency was 91% (106/116) for the drug-coated balloon (DCB) group and 67% (79/118) for the plain balloon catheter group, representing a difference of 24.63% (95% confidence interval, 14.68 to 34.58; P < 0.001). The secondary efficacy end point was primary target lesion patency at 12 months, which was 66% (74/112) for the DCB group and 46% (52/112) for the plain balloon catheter group (95% confidence interval, 6.57 to 32.08; P = 0.004). The mean number of reinterventions per patient to maintain target lesion patency during the 12 months after the index procedure was 0.39 (48/122) in the DCB group and 0.77 (94/122) in the plain balloon catheter group ( P = 0.001). The primary safety end point did not differ between groups ( P = 0.25). CONCLUSIONS: AcoArt Orchid DCB showed better primary patency rates compared with plain balloon angioplasty for treating stenotic lesions in dysfunctional hemodialysis AV fistulae at 6 and 12 months. It required fewer repeated interventions and had comparable safety in 1 year. CLINICAL TRIAL REGISTRY NAME AND REGISTRATION NUMBER: AcoArt III/Arterio-venous Fistula in China, NCT03366727 .

Unveiling practical considerations for reliable and standardized SERS measurements: lessons from a comprehensive review of oblique angle deposition-fabricated silver nanorod array substrates.

Recently, there has been an exponential growth in the number of publications focusing on surface-enhanced Raman scattering (SERS), primarily driven by advancements in nanotechnology and the increasing demand for chemical and biological detection. While many of these publications have focused on the development of new substrates and detection-based applications, there is a noticeable lack of attention given to various practical issues related to SERS measurements and detection. This review aims to fill this gap by utilizing silver nanorod (AgNR) SERS substrates fabricated through the oblique angle deposition method as an illustrative example. The review highlights and addresses a range of practical issues associated with SERS measurements and detection. These include the optimization of SERS substrates in terms of morphology and structural design, considerations for measurement configurations such as polarization and the incident angle of the excitation laser, and exploration of enhancement mechanisms encompassing both intrinsic properties induced by the structure and materials, as well as extrinsic factors arising from wetting/dewetting phenomena and analyte size. The manufacturing and storage aspects of SERS substrates, including scalable fabrication techniques, contamination control, cleaning procedures, and appropriate storage methods, are also discussed. Furthermore, the review delves into device design considerations, such as well arrays, flow cells, and fiber probes, and explores various sample preparation methods such as drop-cast and immersion. Measurement issues, including the effect of excitation laser wavelength and power, as well as the influence of buffer, are thoroughly examined. Additionally, the review discusses spectral analysis techniques, encompassing baseline removal, chemometric analysis, and machine learning approaches. The wide range of AgNR-based applications of SERS, across various fields, is also explored. Throughout the comprehensive review, key lessons learned from collective findings are outlined and analyzed, particularly in the context of detailed SERS measurements and standardization. The review also provides insights into future challenges and perspectives in the field of SERS. It is our hope that this comprehensive review will serve as a valuable reference for researchers seeking to embark on in-depth studies and applications involving their own SERS substrates.

Decoding PFAS contamination via Raman spectroscopy: A combined DFT and machine learning investigation.

In this study, density function theory (DFT) is employed to compute Raman spectra of 40 important Perfluoroalkyl substances (PFASs) as listed in Draft Method 1633 by U.S. Environmental Protection Agent. A systematic comparison of their spectral features is conducted, and Raman peaks and vibrational modes are identified. The Raman spectral regions for the main chemical bonds (such as C-C, CF2 & CF3, O-H) and main functional groups (such as -COOH, -SO3H, -C2H4SO3H, and -SO2NH2) are identified and compared. The impacts of branching location in isomer, molecular chain length, and functional groups on the Raman spectra are analyzed. Particularly, the isomers of PFOA alter the peak locations slightly in wavenumber regions of 200 - 800 and 1000 - 1400 cm-1, while for PFOS, spectral features in the 230 - 360, 470 - 680, and 1030 - 1290 cm-1 regions exhibit significant difference. The carbon chain length can significantly increase the number of Raman peaks, while different functional groups give significantly different peak locations. To facilitate differentiation, a spectral database is constructed by introducing controlled noise into the DFT-computed Raman spectra. Subsequently, two chemometric techniques, principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE), are applied to effectively distinguish among these spectra, both for 40 PFAS compounds and the isomers. The findings demonstrate the promising potential of combining Raman spectroscopy with advanced spectral analysis methods to discriminate between distinct PFAS compounds, holding significant implications for improved PFAS detection and characterization methodologies.

On the Measurements of the Surface-Enhanced Raman Scattering Spectrum: Effective Enhancement Factor, Optical Configuration, Spectral Distortion, and Baseline Variation.

In this paper, a comprehensive theoretical framework for understanding surface-enhanced Raman scattering (SERS) measurements in both solution and thin-film setups, focusing on electromagnetic enhancement principles, was presented. Two prevalent types of SERS substrates found in the literature were investigated: plasmonic colloidal particles, including spherical and spheroid nanoparticles, nanoparticle diameters, and thin-film-based SERS substrates, like ultra-thin substrates, bundled nanorods, plasmonic thin films, and porous thin films. The investigation explored the impact of analyte adsorption, orientation, and the polarization of the excitation laser on effective SERS enhancement factors. Notably, it considered the impact of analyte size on the SERS spectrum by examining scenarios where the analyte was significantly smaller or larger than the hot spot dimensions. The analysis also incorporated optical attenuations arising from the optical properties of the analyte and the SERS substrates. The findings provide possible explanations for many observations made in SERS measurements, such as variations in relative peak intensities during SERS assessments, reductions in SERS intensity at high analyte concentrations, and the occurrence of significant baseline fluctuations. This study offers valuable guidance for optimizing SERS substrate design, enhancing SERS measurements, and improving the quantification of SERS detection.

Identifying Surface-Enhanced Raman Spectra with a Raman Library Using Machine Learning.

Since its discovery, surface-enhanced Raman spectroscopy (SERS) has shown outstanding promise of identifying trace amounts of unknown molecules in rapid, portable formats. However, the many different types of nanoparticles or nanostructured metallic SERS substrates created over the past few decades show substantial variability in the SERS spectra they provide. These inconsistencies have even raised speculation that substrate-specific SERS spectral libraries must be compiled for practical use of this type of spectroscopy. Here, we report a machine learning (ML) algorithm that can identify chemicals by matching their SERS spectra to those of a standard Raman spectral library. We use an approach analogous to facial recognition that utilizes feature extraction in the presence of multiple nuisance variables for spectral recognition. The key element is a metric we call "Characteristic Peak Similarity" (CaPSim) that focuses on the characteristic peaks in the SERS spectra. It has the flexibility to accommodate substrate-specific variability when quantifying the degree of similarity to a Raman spectrum. Analysis shows that CaPSim substantially outperforms existing spectral matching algorithms in terms of accuracy. This ML-based approach could greatly facilitate the spectroscopic identification of molecules in fieldable SERS applications.

A bibliometric and visualization analysis of global research on postherpetic neuralgia from 2000 to 2022: A review.

Postherpetic neuralgia (PHN) represents a notable clinical challenge as it is the most prevalent and severe complication of herpes zoster (HZ). The primary objective was to investigate the current research status and hotspots of PHN research during the period from 2000 to 2022. The literature pertaining to PHN was gathered through the utilization of the Web of Science Core Collection, spanning from January 2000 to December 2022. The software, CiteSpace version 6.2.R2, was employed to produce visual depictions of publications related to PHN across various dimensions such as year, country/region, institution, journal, author, keyword, and reference. This study involved a total of 3505 papers. The USA held a dominant position in the production of scholarly articles. Argentina exhibited the highest frequency of participation in international collaboration. Out of all the institutions, Pfizer exhibited the highest degree of productivity. Harvard University exhibited the highest frequency of participation in international collaboration. The Pain exhibited the most noteworthy productivity rate and citation count among all other journals. Ralf Baron was identified as the most productive author, whereas DWORKIN RH attained the highest citation count. Contemporary scholarly investigations are predominantly centered on identifying risk factors, devising preventative measures, and exploring novel and secure methods of pain management. The current investigation has revealed the focal areas and patterns of studies pertaining to PHN. Presently, the research in this field is focused on identifying the risk factors and preventive measures for PHN, alongside exploring novel and secure pain management strategies.

Analysis of Acupoint Selection and Combinations in Acupuncture Treatment of Piriformis Syndrome: A Protocol for Data Mining.

Background: Piriformis syndrome (PS) is a neuromuscular condition characterized by discomfort in the gluteal region. The efficacy of acupuncture as a treatment modality for PS has been substantiated through a multitude of clinical trials. However, certain queries persist, such as the optimal approach for identifying the most efficacious acupoints. The objective of this study is to perform an initial data mining analysis aimed at identifying the optimal acupoint selection and combinations for the treatment of PS. Methods: We will search 7 electronic bibliographic databases (PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database, Chinese Biomedical Literature Database and Chongqing VIP Database) from inception to June 2023. We will select clinical trials that evaluate the efficacy of acupuncture therapy in the management of PS. Exclusions will be made for reviews, protocols, animal trials, case reports, systematic reviews, and meta-analyses. The primary outcome measure will be clinical outcomes associated with PS. Descriptive statistics will be performed in Excel 2019. Association rule analysis will be performed in SPSS Modeler 18.0. Exploratory factor analysis and cluster analysis will be performed in SPSS Statistics 26.0. Results: This study will investigate the most effective acupoint selection and combinations for patients with PS. Conclusion: Our findings will provide evidence for the effectiveness and potential treatment prescriptions of acupoint application for patients with PS, helping clinicians and patients make a more informed decision together.