A two-stage importance-aware subgraph convolutional network based on multi-source sensors for cross-domain fault diagnosis.

Graph convolutional networks (GCNs) as the emerging neural networks have shown great success in Prognostics and Health Management because they can not only extract node features but can also mine relationship between nodes in the graph data. However, the most existing GCNs-based methods are still limited by graph quality, variable working conditions, and limited data, making them difficult to obtain remarkable performance. Therefore, it is proposed in this paper a two stage importance-aware subgraph convolutional network based on multi-source sensors named I2SGCN to address the above-mentioned limitations. In the real-world scenarios, it is found that the diagnostic performance of the most existing GCNs is commonly bounded by the graph quality because it is hard to get high quality through a single sensor. Therefore, we leveraged multi-source sensors to construct graphs that contain more fault-based information of mechanical equipment. Then, we discovered that unsupervised domain adaptation (UDA) methods only use single stage to achieve cross-domain fault diagnosis and ignore more refined feature extraction, which can make the representations contained in the features inadequate. Hence, it is proposed the two-stage fault diagnosis in the whole framework to achieve UDA. In the first stage, the multiple-instance learning is adopted to obtain the importance factor of each sensor towards preliminary fault diagnosis. In the second stage, it is proposed I2SGCN to achieve refined cross-domain fault diagnosis. Moreover, we observed that deficient and limited data may cause label bias and biased training, leading to reduced generalization capacity of the proposed method. Therefore, we constructed the feature-based graph and importance-based graph to jointly mine more effective relationship and then presented a subgraph learning strategy, which not only enriches sufficient and complementary features but also regularizes the training. Comprehensive experiments conducted on four case studies demonstrate the effectiveness and superiority of the proposed method for cross-domain fault diagnosis, which outperforms the state-of-the art methods.

A fully automated classification of third molar development stages using deep learning.

Accurate classification of tooth development stages from orthopantomograms (OPG) is crucial for dental diagnosis, treatment planning, age assessment, and forensic applications. This study aims to develop an automated method for classifying third molar development stages using OPGs. Initially, our data consisted of 3422 OPG images, each classified and curated by expert evaluators. The dataset includes images from both Q3 (lower jaw left side) and Q4 (lower right side) regions extracted from panoramic images, resulting in a total of 6624 images for analysis. Following data collection, the methodology employs region of interest extraction, pre-filtering, and extensive data augmentation techniques to enhance classification accuracy. The deep neural network model, including architectures such as EfficientNet, EfficientNetV2, MobileNet Large, MobileNet Small, ResNet18, and ShuffleNet, is optimized for this task. Our findings indicate that EfficientNet achieved the highest classification accuracy at 83.7%. Other architectures achieved accuracies ranging from 71.57 to 82.03%. The variation in performance across architectures highlights the influence of model complexity and task-specific features on classification accuracy. This research introduces a novel machine learning model designed to accurately estimate the development stages of lower wisdom teeth in OPG images, contributing to the fields of dental diagnostics and treatment planning.

Multichannel Orthogonal Transform-Based Perceptron Layers for Efficient ResNets.

In this article, we propose a set of transform-based neural network layers as an alternative to the 3 x 3 Conv2D layers in convolutional neural networks (CNNs). The proposed layers can be implemented based on orthogonal transforms, such as the discrete cosine transform (DCT), Hadamard transform (HT), and biorthogonal block wavelet transform (BWT). Furthermore, by taking advantage of the convolution theorems, convolutional filtering operations are performed in the transform domain using elementwise multiplications. Trainable soft-thresholding layers, that remove noise in the transform domain, bring nonlinearity to the transform domain layers. Compared with the Conv2D layer, which is spatial-agnostic and channel-specific, the proposed layers are location-specific and channel-specific. Moreover, these proposed layers reduce the number of parameters and multiplications significantly while improving the accuracy results of regular ResNets on the ImageNet-1K classification task. Furthermore, they can be inserted with a batch normalization (BN) layer before the global average pooling layer in the conventional ResNets as an additional layer to improve classification accuracy.

AuNPs with Cynara scolymus leaf extracts rescue arsenic-induced neurobehavioral deficits and hippocampal tissue toxicity in Balb/c mice through D1R and D2R activation.

The present study was designed to evaluate whether AuNPs (gold nanoparticles) synthesized with the Cynara scolymus (CS) leaf exert protective and/or alleviative effects on arsenic (As)-induced hippocampal neurotoxicity in mice. Neurotoxicity in mice was developed by orally treating 10 mg/kg/day sodium arsenite (NaAsO2) for 21 days. 10 µg/g AuNPs, 1.6 g/kg CS, and 10 µg/g CS-AuNPs were administered orally simultaneously with 10 mg/kg As. CS and CS-AuNPs treatments showed down-regulation of TNF-α and IL-1ß levels. CS and CS-AuNPs also ameliorated apoptosis and reduced the alterations in the expression levels of D1 and D2 dopamine receptors induced by As. Simultaneous treatment with CS and CS-AuNPs improved As-induced learning, memory deficits, and motor coordination in mice assessed by water maze and locomotor tests, respectively. The results of this study provide evidence that CS-AuNPs demonstrated neuroprotective roles with antioxidant, anti-inflammatory, and anti-apoptotic effects, as well as improving D1 and D2 signaling, and eventually reversed neurobehavioral impairments.

Clinical experience of late presentation and major limb complication of Snake bite: A case report and review.

Snakebite is a significant global public health concern. Venomous snake bites can lead to various life-threatening clinical conditions that affect different bodily systems. These include the nervous system (neurotoxicity), musculoskeletal system (myotoxicity), cardiovascular system (cardiotoxicity), and blood clotting mechanisms (haemotoxicity). Here, we report a 5-year-old boy who was bitten by a snake and presented to the Emergency department with complaints of significant infection, necrosis, and gangrene affecting the three fingers of his right hand. After clinical evaluation and investigations, the patient underwent surgical intervention. The patient was discharged from the hospital after 5 weeks of admission with advice to follow up with a primary care physician and physical rehabilitation therapy to ensure the optimal healing and functionality of the affected hand.

Electromagnetically unclonable functions generated by non-Hermitian absorber-emitter.

Physically unclonable functions (PUFs) are a class of hardware-specific security primitives based on secret keys extracted from integrated circuits, which can protect important information against cyberattacks and reverse engineering. Here, we put forward an emerging type of PUF in the electromagnetic domain by virtue of the self-dual absorber-emitter singularity that uniquely exists in the non-Hermitian parity-time (PT)-symmetric structures. At this self-dual singular point, the reconfigurable emissive and absorptive properties with order-of-magnitude differences in scattered power can respond sensitively to admittance or phase perturbations caused by, for example, manufacturing imperfectness. Consequently, the entropy sourced from inevitable manufacturing variations can be amplified, yielding excellent PUF security metrics in terms of randomness and uniqueness. We show that this electromagnetic PUF can be robust against machine learning-assisted attacks based on the Fourier regression and generative adversarial network. Moreover, the proposed PUF concept is wavelength-scalable in radio frequency, terahertz, infrared, and optical systems, paving a promising avenue toward applications of cryptography and encryption.

Validity and Reliability of the Turkish Male Andropause Symptoms Self-Assessment Questionnaire.

Objective: This study was planned to examine the validity and reliability of the Turkish version of the Male Andropause Symptoms Self-Assessment Questionnaire (MASS-Q). Materials and Methods: One hundred and twenty-five men with a mean age of 54.24 ± 6.51 years participated in the study. First, participants' demographic data were recorded. Then, the MASS-Q was adapted to Turkish. The assess the reliability and validity of the Turkish MASS-Q, internal consistency, test-retest reliability, and criterion validity analyses were administered. For the reliability test, the scale was readministered 1 week later. Test-retest reliability was examined with the intraclass correlation coefficients (ICCs). Internal consistency was defined by Cronbach's alpha. Regarding the validity analysis, content validity was determined according to expert opinions. For criterion validity, the Aging Male Symptoms-Questionnaire (AMS-Q) was used. Results: According to the results of the analysis, the ICC values between the test-retest scores of the total and subdimensions (sexual, somatic, psychic, and behavior) of the MASS-Q were found to be 0.987, 0.939, 0.973, 0.951, and 0.887, respectively (P < 0.05). Cronbach's alpha values of the total and subdimensions (sexual, somatic, psychic, and behavior) of the MASS-Q were calculated as 0.924, 0.870, 0.747, 0.865, and 0.667, respectively. According to the ICC values obtained, it was found that the MASS-Q had a high degree of reliability. According to the internal consistency results, the sexual and psychic subdimensions were found to be quite reliable, whereas the somatic and behavioral subdimensions were found to be sufficiently reliable. According to the criterion validity results, a very high and high correlations were found between the AMS-Q scores and the MASS-Q scores (r = 0.636-0.938, P = 0.001). Conclusion: As a result, it was determined that the Turkish version of the MASS-Q is a valid and reliable scale that can be used in Turkish men.

Sorafenib Alleviates Inflammatory Signaling of Tumor Microenvironment in Precancerous Lung Injuries.

According to population-based studies, lung cancer is the prominent reason for cancer-related mortality worldwide in males and is also rising in females at an alarming rate. Sorafenib (SOR), which is approved for the treatment of hepatocellular carcinoma and renal cell carcinoma, is a multitargeted protein kinase inhibitor. Additionally, SOR is the subject of interest for preclinical and clinical trials in lung cancer. This study was designed to assess in vivo the possible effects of sorafenib (SOR) in diethylnitrosamine (DEN)-induced lung carcinogenesis and examine its probable mechanisms of action. A total of 30 adult male rats were divided into three groups (1) control, (2) DEN, and (3) DEN + SOR. The chemical induction of lung carcinogenesis was performed by injection of DEN intraperitoneally at 150 mg/kg once a week for two weeks. The DEN-administered rats were co-treated with SOR of 10 mg/kg by oral gavage for 42 alternate days. Serum and lung tissue samples were analyzed to determine SRY-box transcription factor 2 (SOX-2) levels. The tumor necrosis factor alpha (TNF-α) and interleukin-1 beta (IL-1ß) levels were measured in lung tissue supernatants. Lung sections were analyzed for cyclooxygenase-2 (COX-2) and c-Jun N-terminal kinase (JNK) histopathologically. In addition, cyclooxygenase-2 (COX-2) and c-Jun N-terminal kinase (JNK) were analyzed by immunohistochemistry and immunofluorescence methods, respectively. SOR reduced the level of SOX-2 that maintenance of cancer stemness and tumorigenicity, and TNF-α and IL-1ß levels. Histopathological analysis demonstrated widespread inflammatory cell infiltration, disorganized alveolar structure, hyperemia in the vessels, and thickened alveolar walls in DEN-induced rats. The damage was markedly reduced upon SOR treatment. Further, immunohistochemical and immunofluorescence analysis also revealed increased expression of COX-2 and JNK expression in DEN-intoxicated rats. However, SOR treatment alleviated the expression of these inflammatory markers in DEN-induced lung carcinogenesis. These findings suggested that SOR inhibits DEN-induced lung precancerous lesions through decreased inflammation with concomitant in reduced SOX-2 levels, which enables the maintenance of cancer stem cell properties.

AggregateNet: A deep learning model for automated classification of cervical vertebrae maturation stages.

OBJECTIVE: A study of supervised automated classification of the cervical vertebrae maturation (CVM) stages using deep learning (DL) network is presented. A parallel structured deep convolutional neural network (CNN) with a pre-processing layer that takes X-ray images and the age as the input is proposed. METHODS: A total of 1018 cephalometric radiographs were labelled and classified according to the CVM stages. The images were separated according to gender for better model-fitting. The images were cropped to extract the cervical vertebrae automatically using an object detector. The resulting images and the age inputs were used to train the proposed DL model: AggregateNet with a set of tunable directional edge enhancers. After the features of the images were extracted, the age input was concatenated to the output feature vector. To have the parallel network not overfit, data augmentation was used. The performance of our CNN model was compared with other DL models, ResNet20, Xception, MobileNetV2 and custom-designed CNN model with the directional filters. RESULTS: The proposed innovative model that uses a parallel structured network preceded with a pre-processing layer of edge enhancement filters achieved a validation accuracy of 82.35% in CVM stage classification on female subjects, 75.0% in CVM stage classification on male subjects, exceeding the accuracy achieved with the other DL models investigated. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If AggregateNet is used without directional filters, the test accuracy decreases to 80.0% on female subjects and to 74.03% on male subjects. CONCLUSION: AggregateNet together with the tunable directional edge filters is observed to produce higher accuracy than the other models that we investigated in the fully automated determination of the CVM stages.

Artichoke (Cynara Scolymus) Methanolic Leaf Extract Alleviates Diethylnitrosamine-Induced Toxicity in BALB/c Mouse Brain: Involvement of Oxidative Stress and Apoptotically Related Klotho/PPARγ Signaling.

(1) Background: Various epidemiological studies suggest that oxidative stress and disrupted neuronal function are mechanistically linked to neurodegenerative diseases (NDs), including Parkinson's disease (PD) and Alzheimer's disease (AD). DNA damage, oxidative stress, lipid peroxidation, and eventually, cell death such as NDs can be induced by nitrosamine-related compounds, leading to neurodegeneration. A limited number of studies have reported that exposure to diethylnitrosamine (DEN), which is commonly found in processed/preserved foods, causes biochemical abnormalities in the brain. Artichoke leaves have been used in traditional medicine as a beneficial source of bioactive components such as hydroxycinnamic acids, cynarine, chlorogenic acid, and flavonoids (luteolin and apigenin). The aim of this study is to investigate the favorable effects of exogenous artichoke (Cynara scolymus) methanolic leaf extract supplementation in ameliorating DEN-induced deleterious effects in BALB/c mouse brains. (2) Methods: This study was designed to evaluate DEN (toxicity induction by 100 mg/kg) and artichoke (protective effects of 0.8 and 1.6 g/kg treatment) for 14 days. All groups underwent a locomotor activity test to evaluate motor activity. In brain tissue, oxidative stress indicators (TAC, TOS, and MDA), Klotho and PPARγ levels, and apoptotic markers (Bax, Bcl-2, and caspase-3) were measured. Brain slices were also examined histopathologically. (3) Results: Artichoke effectively ameliorated DEN-induced toxicity with increasing artichoke dose. Impaired motor function and elevated oxidative stress markers (decreasing MDA and TOS levels and increasing TAC level) induced by DEN intoxication were markedly restored by high-dose artichoke treatment. Artichoke significantly improved the levels of Klotho and PPARγ, which are neuroprotective factors, in mouse brain tissue exposed to DEN. In addition, caspase-3 and Bax levels were reduced, whereas the Bcl-2 level was elevated with artichoke treatment. Furthermore, recovery was confirmed by histopathological analysis. (4) Conclusions: Artichoke exerted neuroprotective effects against DEN-induced brain toxicity by mitigating oxidant parameters and exerting antioxidant and antiapoptotic effects. Further research is needed to fully identify the favorable impact of artichoke supplementation on all aspects of DEN brain intoxication.

Fully automated determination of the cervical vertebrae maturation stages using deep learning with directional filters.

INTRODUCTION: We aim to apply deep learning to achieve fully automated detection and classification of the Cervical Vertebrae Maturation (CVM) stages. We propose an innovative custom-designed deep Convolutional Neural Network (CNN) with a built-in set of novel directional filters that highlight the edges of the Cervical Vertebrae in X-ray images. METHODS: A total of 1018 Cephalometric radiographs were labeled and classified according to the Cervical Vertebrae Maturation (CVM) stages. The images were cropped to extract the cervical vertebrae using an Aggregate Channel Features (ACF) object detector. The resulting images were used to train four different Deep Learning (DL) models: our proposed CNN, MobileNetV2, ResNet101, and Xception, together with a set of tunable directional edge enhancers. When using MobileNetV2, ResNet101 and Xception, data augmentation is adopted to allow adequate network complexity while avoiding overfitting. The performance of our CNN model was compared with that of MobileNetV2, ResNet101 and Xception with and without the use of directional filters. For validation and performance assessment, k-fold cross-validation, ROC curves, and p-values were used. RESULTS: The proposed innovative model that uses a CNN preceded with a layer of tunable directional filters achieved a validation accuracy of 84.63%84.63% in CVM stage classification into five classes, exceeding the accuracy achieved with the other DL models investigated. MobileNetV2, ResNet101 and Xception used with directional filters attained accuracies of 78.54%, 74.10%, and 80.86%, respectively. The custom-designed CNN method also achieves 75.11% in six-class CVM stage classification. The effectiveness of the directional filters is reflected in the improved performance attained in the results. If the custom-designed CNN is used without the directional filters, the test accuracy decreases to 80.75%. In the Xception model without the directional filters, the testing accuracy drops slightly to 79.42% in the five-class CVM stage classification. CONCLUSION: The proposed model of a custom-designed CNN together with the tunable Directional Filters (CNNDF) is observed to provide higher accuracy than the commonly used pre-trained network models that we investigated in the fully automated determination of the CVM stages.

Synthesis of New Cinnamoyl-Amino Acid Conjugates and in Vitro Cytotoxicity and Genotoxicity Studies.

Amino acid conjugates are described by the reaction of amino acids with bioactive organic groups such as vitamins, hormones, flavonoids, steroids, and sugars. In this study, 12 new conjugates were synthesized by reaction of cinnamic acid derivatives with various amino acids. Cytotoxic studies against four different human cancer cells (MCF7, PC-3, Caco-2, and A2780) were carried out by MTT assay method at five different concentrations. The structure-activity relationships based on the cell viability rates were evaluated. To compare the anticancer activities of the compounds using computational chemistry methods, they were docked against A2780 human ovarian cancer, Michigan Cancer Foundation-7 (MCF7), human prostate cancer (PC-3) and human colon epidermal adenocarcinoma (Caco-2) cell lines and compared with the standard 5-Fluorouracil. The results indicate that the efficacy of cinnamic acid derivatives increases with the presence of amino acids. Comet assay was conducted to understand whether the cell deaths occur through DNA damage mechanism and the results exhibit that the changes in the specified parameters were statistically significant (p<0.05). Our study demonstrated that the compounds cause cell death through the formation of DNA damage mechanism.

Improving a cortical pyramidal neuron model's classification performance on a real-world ecg dataset by extending inputs.

Pyramidal neurons display a variety of active conductivities and complex morphologies that support nonlinear dendritic computation. Given growing interest in understanding the ability of pyramidal neurons to classify real-world data, in our study we applied both a detailed pyramidal neuron model and the perceptron learning algorithm to classify real-world ECG data. We used Gray coding to generate spike patterns from ECG signals as well as investigated the classification performance of the pyramidal neuron's subcellular regions. Compared with the equivalent single-layer perceptron, the pyramidal neuron performed poorly due to a weight constraint. A proposed mirroring approach for inputs, however, significantly boosted the classification performance of the neuron. We thus conclude that pyramidal neurons can classify real-world data and that the mirroring approach affects performance in a way similar to non-constrained learning.

3D pull-out finite element simulation of the pedicle screw-trabecular bone interface at strain rates.

Biomedical experimental studies such as pull-out (PO), screw loosening experience variability mechanical properties of fresh bone, legal procedures of cadaver bone samples and time-consuming problems. Finite Element Method (FEM) could overcome experimental problems in biomechanics. However, material modelling of bone is quite difficult, which has viscoelastic and viscoplastic properties. The study presents a bone material model which is constructed at the strain rates with the Johnson-Cook (JC) material model, one of the robust constitutive material models. The JC material constants of trabecular bone are determined by the curve fitting method at strain rates for the 3D PO finite element simulation, which defines the screw-bone interface relationship. The PO simulation is performed using the Abaqus/CAE software program. Bone fracture mechanisms are simulated with dynamic/explicit solutions during the PO phenomenon. The paper exposes whether the strain rate has effects on the PO performance. Moreover, simulation reveals the relationship between pedicle screw diameter and PO performance. The results obtained that the maximum pull-out force (POF) improves as both the screw diameter and the strain rate increase. For 5.5 mm diameter pedicle screw POFs were 487, 517 and 1708 N at strain rate 0.00015, 0.015 and 0.015 s-1, respectively. The FOFs obtained from the simulation of the other screw were 730, 802 and 2008 N at strain rates 0.00015, 0.0015 and 0.015, respectively. PO phenomenon was also simulated realistically in the finite element analysis (FEA).

Experimental investigation of pull-out performance of pedicle screws at different polyurethane (PU) foam densities.

Pedicle bone screws are one of the most critical materials used in spinal orthopaedic operations. Screw loosening and pull-out (PO) are basic complications encountered during or after surgery. Pull-out Strength (POS) of the bone is one of the significant parameters to understand the mechanical behaviour of a screw fixed to poor quality or osteoporotic bone. This study investigates how the POS of a pedicle screw is affected by the factors of the screw diameter and the polyurethane (PU) foam density by experimental analysis. In the experiments, two different diameter (5.5 and 6.5 mm) of conical pedicle screws and five different density (0.08, 0.16, 0.24, 0.32 and 0.48 g·cm-3) PU foams were used. According to the force-displacement curves obtained from experimental results, the POS increased with the increases in screw diameter and PU foam density.

Retrospective analysis of the effect of breast surgery on body posture in patients with early-stage breast cancer after cancer treatment (VENUS study) (Breast cancer and body posture)

Background/aim: The aim of this study was to determine whether breast surgery changes body posture in patients with early-stage breast cancer. Materials and methods: Study variables include age, side and localization of the tumor in the breast, applied breast surgery, axillary interference, pathological tumor size, axillary lymph node metastasis, body mass index, bone density, adjuvant therapies, and histological type. Thoracic kyphosis angle due to the anatomically affected primary region to detect changes in body posture and Cobb's method were used to measure this. Results: There was a statistically significant difference in the mean Cobb's angle between the follow-up times of 57 patients (P < 0.001), with a cumulative increase in the Cobb's angle from baseline to the second year. As the age of the diagnosis progressed, the Cobb's angle increased significantly at 2 years when compared to baseline (r = 0,616, P < 0,001). In terms of baseline, the higher the BMI level in the 2nd year, the higher the Cobb's angle in the 2nd year as compared to the baseline (r = 0,529, P < 0,001). Conclusion: It was concluded that the increase in thoracic kyphosis in patients with breast cancer should be examined psychosocially. The study should be supported by a larger number of patients.

Regulatory consideration on preparation and clinical use of COVID-19 convalescent plasma.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), the cause of coronavirus disease (COVID-19), spreading from Wuhan to worldwide has been emerged since December 2019. Although scientists and researchers have been racing to develop specific therapeutic agents or vaccines against SARS-CoV-2 since the identification of the agent, either a drug or a vaccine has not been approved to treat or to prevent COVID-19 up to date. On the base of historical experiences, Convalescent Plasma (CP), a passive antibody therapy, has been evaluated as a hopeful and potential therapeutic option since the beginning of the COVID-19 outbreak. Immune plasma had been used previously for the treatment of H1N1 influenza virus, SARS-CoV-1 and MERS-CoV epidemics successfully. In this scope competent authorities are responsible to set up certain principles and criteria for the collection and clinical use of COVID-19 Convalescent Plasma (CCP). This document has been prepared to aid both for the convalescent plasma suppliers and the clinicians. The first part encompasses the supply of CCP and the second part lead the clinical use of CCP for the treatment of patients with severe COVID-19 infection. Turkish Ministry of Health developed a guide on collection and clinical use of CCP and created a web-based monitoring system to follow-up the patients treated with convalescent plasma in universal. This follow-up process is thought to be crucial for the creation and development of current and future treatment modalities. This guide would be a pathfinder for clinicians and/or institutions those eager to conduct CCP treatment more effectively.

Computationally Efficient Wildfire Detection Method Using a Deep Convolutional Network Pruned via Fourier Analysis.

In this paper, we propose a deep convolutional neural network for camera based wildfire detection. We train the neural network via transfer learning and use window based analysis strategy to increase the fire detection rate. To achieve computational efficiency, we calculate frequency response of the kernels in convolutional and dense layers and eliminate those filters with low energy impulse response. Moreover, to reduce the storage for edge devices, we compare the convolutional kernels in Fourier domain and discard similar filters using the cosine similarity measure in the frequency domain. We test the performance of the neural network with a variety of wildfire video clips and the pruned system performs as good as the regular network in daytime wild fire detection, and it also works well on some night wild fire video clips.

Synthesis of some novel pyridine compounds containing bis-1,2,4-triazole/thiosemicarbazide moiety and investigation of their antioxidant properties, carbonic anhydrase, and acetylcholinesterase enzymes inhibition profiles.

Some novel derivatives of thiosemicarbazide and 1,2,4-triazole-3-thiol were synthesized and evaluated for their biological activities. The title compounds were prepared starting from readily available pyridine-2,5-dicarboxylic acid. The reaction carboxylic acid with absolute ethanol afforded the corresponding dimethyl pyridine-2,5-dicarboxylate (1). The reaction of dimethyl-2,5-pyridinedicarboxylate (1) with hydrazine hydrate good yielded pyridine-2,5-dicarbohydrazide (2). Refluxing compound 2 with alkyl/aryl isothiocyanate derivatives for 3-8 h afforded 1,4-disubstituted thiosemicarbazides (3a-e). Base-catalyzed intra-molecular dehydrative cyclization of these intermediates furnished the 4,5-disubstituted bis-mercaptotriazoles (4a-e) in good yield (85%-95%). Among the target compounds, 2,2'-(pyridine-2,5-diyldicarbonyl)bis[N-(p-methoxyphenyl)hydrazinecarbothioamide] (3c) showed very high activity with value of 72.93% against 1,1-diphenyl-2-picrylhydrazyl free radical at the concentration of 25 µg/mL. The inhibitory effects of the target compounds against acetylcholinesterase (AChE), hCA I, and II were studied. AChE, cytosolic hCA I and II isoforms were potently inhibited by synthesized these derivatives with Ki s in the range of 3.07 ± 0.76-87.26 ± 29.25 nM against AChE, in the range of 1.47 ± 0.37-10.06 ± 2.96 nM against hCA I, and in the range of 3.55 ± 0.57-7.66 ± 2.06 nM against hCA II, respectively.