Search Results (19)

Cancer of the uterine cervix (cervical cancer) is a leading cancer among women worldwide, although its incidence has been reducing in many developing nations. In the majority of cervical cancer cases, the presence of high-risk human papillomavirus (HPV) is usually detected. However, a growing body of evidence currently considers that exclusive HPV infection may not be sufficient for cancer development. Apart from certain common risk factors for cervical cancer, like poor nutritional status and smoking, many studies documented an association with other viral infections, such as human immunodeficiency virus (HIV) and herpes simplex virus type 2 (HSV-2). Similarly, vaginal bacterial populations perhaps play a key role in cervical cancer. It may be worth mentioning that different bacterial species can immensely influence (either protecting or adversely) the biochemical characteristics of the cervicovaginal environment—for example, Lactobacillus crispatus, Gardnerella vaginalis, and Chlamydia trachomatis. As a result, chronic infections with unfavorable microorganisms (other than HPV) may affect the pathological processes of malignancy. On the other hand, the cervix is an estrogen-sensitive organ like the corpus uteri (i.e., the body of the uterus). Estrogen and different estrogen receptors are implicated in the development and promotion of various cancers, including endometrial cancer. A number of reports also suggest a close association between estrogen and HPV in the development of cervical cancer. Furthermore, estrogen is linked with the characteristics of the vaginal microenvironment including bacteria. Therefore, several of the abovementioned factors (some are preventable) could play an important role in the progression of cervical neoplastic lesions. Full article

In the mid-to-late Ming Dynasty, Yunqi Zhuhong 雲栖祩宏 and Ouyi Zhixu 蕅益智旭 integrated Zen thought and Pure Land Buddhism based on the fusion of various Buddhist sects, which facilitated the transition to Chan Jing He Yi (integration of Zen and Pure Land Buddhism 禪淨合一). In this context, Journey to the West 西遊記, published in the late Ming Dynasty, reflects the characteristic of Chan Jing He Yi (integration of Zen and Pure Land Buddhism 禪淨合一). Based on the historical fact that the monk Xuanzang 玄奘 journeyed to India to seek Buddhist scriptures during the Tang Dynasty’s Zhenguan period, four relatively complete literary works that recount the stories of this westward journey were published over nearly a thousand years, from the Tang Dynasty to the Ming Dynasty: Da Ci En Si San Zang Fa Shi Zhuan 大慈恩寺三藏法師傳, Da Tang San Zang Qu Jing Shi Hua 大唐三藏取經詩話, the Journey to the West drama 西遊記雜劇, and Journey to the West. The Buddhist ideas in these four works went through a transformation from advocating yoga theory 瑜伽論 to advocating belief in Vaisravana 毗沙門天王信仰 and then to focusing on the “mind nature 心性” theory of Zen Buddhism. Finally, in Journey to the West, Buddhist thought is aimed at achieving rebirth in the Western Pure Land and supplemented with Chan Buddhist practices, which are aligned with the trend of Chan Jing He Yi (integration of Zen and Pure Land Buddhism 禪淨合一). In Journey to the West, the concepts of Ming Xin Jian Xing (find one’s true self 明心見性) and Ji Xin Ji Fo (the mind is the Buddha 即心即佛) differ from the Zen Buddhism concept of seeing one’s own nature. Instead, it requires seeking other Buddhas and ascending to the Western Pure Land to meet Amitabha Buddha in order to achieve complete spiritual cultivation. This had changed from the Wei Xin Jing Tu (mind-only Pure Land 唯心淨土) theory advocated by Zen Buddhism to the Xi Fang Jing Tu (Western Pure Land 西方淨土) theory advocated by the Pure Land School. The numerous depictions of Pure Land cultivation methods, such as Cheng Ming Nian Fo (chanting the name of Amitabha Buddha 稱名念佛), Chi Jie (commandment keeping 持戒), and the Pure Land reincarnation-type Guanyin faith 淨土往生型觀音信仰, also appear in Journey to the West, reflecting the profound influence of Chan Jing He Yi 禪淨合一 in the mid-to-late Ming Dynasty on Journey to the West. Full article

The gastrointestinal and respiratory systems are closely linked in different ways, including from the embryological, anatomical, cellular, and physiological angles. The highest number (and various types) of microorganisms live in the large intestine/colon, and constitute the normal microbiota in healthy people. Adverse alterations of the microbiota or dysbiosis can lead to chronic inflammation. If this detrimental condition persists, a sequence of pathological events can occur, such as inflammatory bowel disease, dysplasia or premalignant changes, and finally, cancer. One of the most commonly identified bacteria in both inflammatory bowel disease and colon cancer is Escherichia coli. On the other hand, patients with inflammatory bowel disease are at risk of several other diseases—both intestinal (such as malnutrition and intestinal obstruction, besides cancer) and extraintestinal (such as arthritis, bronchiectasis, and cancer risk). Cancers of the lung and colon are the two most common malignancies occurring worldwide (except for female breast cancer). Like the bacterial role in colon cancer, many studies have shown a link between chronic Chlamydia pneumoniae infection and lung cancer. However, in colon cancer, genotoxic colibactin-producing E. coli belonging to the B2 phylogroup may promote tumorigenesis. Furthermore, E. coli is believed to play an important role in the dissemination of cancer cells from the primary colonic site. Currently, seven enteric pathogenic E. coli subtypes have been described. Conversely, three Chlamydiae can cause infections in humans (C. trachomatis may increase the risk of cervical and ovarian cancers). Nonetheless, striking genomic plasticity and genetic modifications allow E. coli to constantly adjust to the surrounding environment. Consequently, E. coli becomes resistant to antibiotics and difficult to manage. To solve this problem, scientists are thinking of utilizing suitable lytic bacteriophages (viruses that infect and kill bacteria). Several bacteriophages of E. coli and Chlamydia species are being evaluated for this purpose. Full article

The optimal therapy for patients with non-metastatic biochemically relapsed prostate cancer (BRPC-M0) after local therapy is elusive. Thus, the evaluation of new non-toxic compounds in BRPC-M0 patients is warranted. PectaSol^®-Modified citrus pectin (P-MCP) is a food supplement categorized as GRAS (Generally Recognized As Safe) by the FDA. It is a competitive inhibitor of the galectin-3 protein, which is involved in cancer pathogenesis. In an early report of the present phase 2 study, P-MCP treatment for 6 months led to prostate-specific antigen doubling time (PSADT) improvement in 75% of patients with BRPC-M0. Herein, we report the second long-term treatment phase of an additional 12 months of P-MCP therapy (4.8 g × 3/day orally) in patients without disease progression after the initial 6 months of therapy. Of the 46 patients that entered the second treatment phase, 7 patients withdrew consent and decided to continue therapy out of pocket, and 39 initiated the second treatment phase. After a total of 18 months of P-MCP treatment, 85% (n = 33) had a durable long-term response, with 62% (n = 24) showing decreased/stable PSA, 90% (n = 35) PSADT improvement, and all with negative scans. No patient had grade 3/4 toxicity. In conclusion, P-MCP may have long-term durable efficacy and is safe in BRPC-M0. Full article

In cancer development and its clinical course, bacteria can be involved in etiology and secondary infection. Regarding etiology, various epidemiological studies have revealed that Helicobacter pylori can directly impact gastric carcinogenesis. The Helicobacter pylori-associated virulence factor cytotoxin-associated gene A perhaps plays an important role through different mechanisms such as aberrant DNA methylation, activation of nuclear factor kappa B, and modulation of the Wnt/β-catenin signaling pathway. Many other bacteria, including Salmonella and Pseudomonas, can also affect Wnt/β-catenin signaling. Although Helicobacter pylori is involved in both gastric adenocarcinoma and mucosa-associated lymphoid tissue lymphoma, its role in the latter disease is more complicated. Among other bacterial species, Chlamydia is linked with a diverse range of diseases including cancers of different sites. The cellular organizations of Chlamydia are highly complex. Interestingly, Escherichia coli is believed to be associated with colon cancer development. Microorganisms such as Escherichia coli and Pseudomonas aeruginosa are frequently isolated from secondary infections in cancer patients. In these patients, the common sites of infection are the respiratory, gastrointestinal, and urinary tracts. There is an alarming rise in infections with multidrug-resistant bacteria and the scarcity of suitable antimicrobial agents adversely influences prognosis. Therefore, effective implementation of antimicrobial stewardship strategies is important in cancer patients. Full article

Tomato leaf diseases can incur significant financial damage by having adverse impacts on crops and, consequently, they are a major concern for tomato growers all over the world. The diseases may come in a variety of forms, caused by environmental stress and various pathogens. An automated approach to detect leaf disease from images would assist farmers to take effective control measures quickly and affordably. Therefore, the proposed study aims to analyze the effects of transformer-based approaches that aggregate different scales of attention on variants of features for the classification of tomato leaf diseases from image data. Four state-of-the-art transformer-based models, namely, External Attention Transformer (EANet), Multi-Axis Vision Transformer (MaxViT), Compact Convolutional Transformers (CCT), and Pyramid Vision Transformer (PVT), are trained and tested on a multiclass tomato disease dataset. The result analysis showcases that MaxViT comfortably outperforms the other three transformer models with $97\%$ overall accuracy, as opposed to the $89\%$ accuracy achieved by EANet, $91\%$ by CCT, and $93\%$ by PVT. MaxViT also achieves a smoother learning curve compared to the other transformers. Afterwards, we further verified the legitimacy of the results on another relatively smaller dataset. Overall, the exhaustive empirical analysis presented in the paper proves that the MaxViT architecture is the most effective transformer model to classify tomato leaf disease, providing the availability of powerful hardware to incorporate the model. Full article

Electroencephalogram (EEG)-based emotion recognition is a computationally challenging issue in the field of medical data science that has interesting applications in cognitive state disclosure. Generally, EEG signals are classified from frequency-based features that are often extracted using non-parametric models such as Welch’s power spectral density (PSD). These non-parametric methods are not computationally sound due to having complexity and extended run time. The main purpose of this work is to apply the multiple signal classification (MUSIC) model, a parametric-based frequency-spectrum-estimation technique to extract features from multichannel EEG signals for emotional state classification from the SEED dataset. The main challenge of using MUSIC in EEG feature extraction is to tune its parameters for getting the discriminative features from different classes, which is a significant contribution of this work. Another contribution is to show some flaws of this dataset for the first time that contributed to achieving high classification accuracy in previous research works. This work used MUSIC features to classify three emotional states and achieve 97% accuracy on average using an artificial neural network. The proposed MUSIC model optimizes a 95–96% run time compared with the conventional classical non-parametric technique (Welch’s PSD) for feature extraction. Full article

Articular cartilage shows limited self-healing ability owing to its low cellularity and avascularity. Untreated cartilage defects display an increased propensity to degenerate, leading to osteoarthritis (OA). During OA progression, articular chondrocytes are subjected to significant alterations in gene expression and phenotype, including a shift towards a hypertrophic-like state (with the expression of collagen type X, matrix metalloproteinases-13, and alkaline phosphatase) analogous to what eventuates during endochondral ossification. Present OA management strategies focus, however, exclusively on cartilage inflammation and degradation. A better understanding of the hypertrophic chondrocyte phenotype in OA might give new insights into its pathogenesis, suggesting potential disease-modifying therapeutic approaches. Recent developments in the field of cellular/molecular biology and tissue engineering proceeded in the direction of contrasting the onset of this hypertrophic phenotype, but knowledge gaps in the cause–effect of these processes are still present. In this review we will highlight the possible advantages and drawbacks of using this approach as a therapeutic strategy while focusing on the experimental models necessary for a better understanding of the phenomenon. Specifically, we will discuss in brief the cellular signaling pathways associated with the onset of a hypertrophic phenotype in chondrocytes during the progression of OA and will analyze in depth the advantages and disadvantages of various models that have been used to mimic it. Afterwards, we will present the strategies developed and proposed to impede chondrocyte hypertrophy and cartilage matrix mineralization/calcification. Finally, we will examine the future perspectives of OA therapeutic strategies. Full article

In order to provide intelligent and efficient healthcare services in the Internet of Medical Things (IoMT), human action recognition (HAR) can play a crucial role. As a result of their stringent requirements, such as high computational complexity and memory efficiency, classical HAR techniques are not applicable to modern and intelligent healthcare services, e.g., IoMT. To address these issues, we present in this paper a novel HAR technique for healthcare services in IoMT. This model, referred to as the spatio-temporal graph convolutional network (STGCN), primarily aims at skeleton-based human–machine interfaces. By independently extracting spatial and temporal features, STGCN significantly reduces information loss. Spatio-temporal information is extracted independently of the exact spatial and temporal point, ensuring the extraction of useful features for HAR. Using only joint data and fewer parameters, we demonstrate that our proposed STGCN achieved 92.2% accuracy on the skeleton dataset. Unlike multi-channel methods, which use a combination of joint and bone data and have a large number of parameters, multi-channel methods use both joint and bone data. As a result, STGCN offers a good balance between accuracy, memory consumption, and processing time, making it suitable for detecting medical conditions. Full article

Nardostachys jatamansi is a critically endangered medicinal plant and endemic to the Himalayas, having high commercial demand globally. The accumulation of various secondary metabolites in its shoots and roots with antioxidant potential are well-documented in traditional as well as modern medicine systems. In the present study, we first attempted to investigate the impact of citrate (−ve charge, 11.1 ± 1.9 nm) and CTAB (+ve charge, 19.5 ± 3.2 nm) coated gold nanoparticles (AuNPs) on the in vitro proliferation and antioxidant activities of N. jatamansi. Both the nanoparticles differentially affected the morphological and biochemical parameters, chlorophyll content, internal hormone concentration, and antioxidant activities in a concentration-dependent (10–100 µM) manner. Vigorous shooting was observed in half strength MS medium supplemented with IAA (1 mg/L) with 60 µM citrate-AuNPs (46.4 ± 3.7 mm) and 40 µM CTAB-AuNPs (42.2 ± 3.2 mm). Similarly, the maximum number of roots (5.00 ± 0.67 and 5.33 ± 0.58) and root length (29.9 ± 1.5 mm and 27.3 ± 4.8 mm) was reported in half-strength MS medium with IAA (1 mg/L) supplemented with 60 µM citrate-AuNPs and 40 µM CTAB-AuNPs, respectively. In addition, plants growing on MS medium supplemented with 60 µM citrate-AuNPs and 40 µM CTAB-AuNPs showed significantly enhanced photosynthetic pigments (chlorophyll a and b, carotenoids, and total chlorophyll), internal hormone concentration (GA3, IAA, and ABA), and antioxidant activities (total phenolics, flavonoids, DPPH, and SOD enzyme activity). Moreover, the transcript analysis of ANR1, ARF18, PLY9, SAUR28, GID1A, GRF1, SOD, and CAT further confirmed the role of 60 µM citrate-AuNPs and 40 µM CTAB-AuNPs in the improvement in the growth and antioxidant activities of N. jatamansi. Bearing in mind the urgent requirements of the effective conservation measures of this endangered species, the present findings suggest the elicitation of citrate-AuNPs and CTAB-AuNPs would significantly improve the potential applications of N. jatamansi in the medicinal plant-based industry. Full article

Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets. Full article

In this study, we have specifically blocked a key step of sphingolipid (SL) biosynthesis in Candida glabrata by disruption of the orthologs of ScIpt1 and ScSkn1. Based on their close homology with S. cerevisiae counterparts, the proteins are predicted to catalyze the addition of a phosphorylinositol group onto mannosyl inositolphosphoryl ceramide (MIPC) to form mannosyl diinositolphosphoryl ceramide (M(IP)₂C), which accounts for the majority of complex SL structures in S. cerevisiae membranes. High throughput lipidome analysis confirmed the accumulation of MIPC structures in ΔCgipt1 and ΔCgskn1 cells, albeit to lesser extent in the latter. Noticeably, ΔCgipt1 cells showed an increased susceptibility to azoles; however, ΔCgskn1 cells showed no significant changes in the drug susceptibility profiles. Interestingly, the azole susceptible phenotype of ΔCgipt1 cells seems to be independent of the ergosterol content. ΔCgipt1 cells displayed altered lipid homeostasis, increased membrane fluidity as well as high diffusion of radiolabeled fluconazole (³H-FLC), which could together influence the azole susceptibility of C. glabrata. Furthermore, in vivo experiments also confirmed compromised virulence of the ΔCgipt1 strain. Contrarily, specific functions of CgSkn1 remain unclear. Full article

Extensive research has been conducted in the past to determine age, gender, and words spoken in Bangla speech, but no work has been conducted to identify the regional language spoken by the speaker in Bangla speech. Hence, in this study, we create a dataset containing 30 h of Bangla speech of seven regional Bangla dialects with the goal of detecting synthesized Bangla speech and categorizing it. To categorize the regional language spoken by the speaker in the Bangla speech and determine its authenticity, the proposed model was created; a Stacked Convolutional Autoencoder (SCAE) and a Sequence of Multi-Label Extreme Learning machines (MLELM). SCAE creates a detailed feature map by identifying the spatial and temporal salient qualities from MFEC input data. The feature map is then sent to MLELM networks to generate soft labels and then hard labels. As aging generates physiological changes in the brain that alter the processing of aural information, the model took age class into account while generating dialect class labels, increasing classification accuracy from 85% to 95% without and with age class consideration, respectively. The classification accuracy for synthesized Bangla speech labels is 95%. The proposed methodology works well with English speaking audio sets as well. Full article

Leishmania species are causative agents of human leishmaniasis, affecting 12 million people annually. Drugs available for leishmaniasis are toxic, and no vaccine is available. Thus, the major thrust is to identify new therapeutic targets. Leishmania is an auxotroph for heme and must acquire heme from the host for its survival. Thus, the major focus has been to understand the heme acquisition process by the parasites in the last few decades. It is conceivable that the parasite is possibly obtaining heme from host hemoprotein, as free heme is not available in the host. Current understanding indicates that Leishmania internalizes hemoglobin (Hb) through a specific receptor by a clathrin-mediated endocytic process and targets it to the parasite lysosomes via the Rab5 and Rab7 regulated endocytic pathway, where it is degraded to generate intracellular heme that is used by the parasite. Subsequently, intra-lysosomal heme is initially transported to the cytosol and is finally delivered to the mitochondria via different heme transporters. Studies using different null mutant parasites showed that these receptors and transporters are essential for the survival of the parasite. Thus, the heme acquisition process in Leishmania may be exploited for the development of novel therapeutics. Full article

A series of chelated dehydroacetic acid–imine-based ligands L¹H~L⁴H was synthesized by reacting dehydroacetic acid with 2-t-butylaniline, (S)-1-phenyl-ethylamine, 4-methoxylbenzylamine, and 2-(aminoethyl)pyridine, respectively, in moderate yields. Ligands L¹H~L⁴H reacted with AlMe₃ in toluene to afford corresponding compounds AlMe₂L¹ (1), AlMe₂L² (2), AlMe₂L³ (3), and AlMe₂L⁴ (4). All the ligands and aluminum compounds were characterized by IR spectra, ¹H and ¹³C NMR spectroscopy. Additionally, the ligands L¹H~L⁴H and corresponding aluminum derivatives 1, 3, and 4 were characterized by single-crystal X-ray diffractometry. The catalytic activities using these aluminum compounds as catalysts for the ε-caprolactone ring-opening polymerization (ROP) and styrene oxide-CO₂ coupling reactions were studied. The results show that increases in the reaction temperature and selective solvent intensify the conversions of ε-caprolactone to polycaprolactone. Regarding the coupling reactions of styrene oxide and CO₂, the conversion rate is over 90% for a period of 12 h at 90 °C. This strategy dispenses the origination of cyclic styrene carbonates, which is an appealing concern because of the transformation of CO₂ into an inexpensive, renewable and easy excess carbon feedstock. Full article