NLR, MLR, and PLR are adverse prognostic variables for sleeve lobectomy within non-small cell lung cancer.

BACKGROUND: The goal of the research was to examine the value of peripheral blood indicators in forecasting survival and recurrence among people suffering central-type non-small cell lung cancer (NSCLC) undergoing sleeve lobectomy (SL). METHODS: Clinical information was gathered from 146 individuals suffering from NSCLC who had SL at our facility between January 2014 and May 2023. Peripheral blood neutrophil lymphocyte ratio (NLR), monocyte lymphocyte ratio (MLR), and platelet lymphocyte ratio (PLR) levels were determined by receiver operating characteristic (ROC) curve to establish the threshold points. Kaplan-Meier survival analysis was employed to evaluate the prognostic value of different groupings, and both univariate and multivariate Cox proportional hazards model (referred to as COX) were performed. RESULTS: The disease-free survival (DFS) and overall survival (OS) cutoff values were carried out via ROC analysis. Kaplan-Meier survival analysis revealed notable differences in OS for NLR (≥2.196 vs. <2.196, p = 0.0009), MLR (≥0.2763 vs. <0.2763, p = 0.0018), and PLR (≥126.11 vs. <126.11, p = 0.0354). Similarly, significant differences in DFS were observed for NLR (≥3.010 vs. <3.010, p = 0.0005), MLR (≥0.2708 vs. <0.2708, p = 0.0046), and PLR (≥126.11 vs. <126.11, p = 0.0028). Univariate Cox analysis showed that NLR (hazard ratio [HR]: 2.469; 95% confidence interval [CI]: 1.416-4.306, p < 0.001), MLR (HR: 2.192, 95% CI: 1.319-3.643, p = 0.002) and PLR (HR: 1.696, 95% CI: 1.029-2.795, p = 0.038) were correlated alongside OS. Multivariate Cox analysis showed that NLR (HR: 2.036, 95% CI: 1.072-3.864, p = 0.030) was a separate OS risk variable. Additionally, the pN stage (HR: 3.163, 95% CI: 1.660-6.027, p < 0.001), NLR (HR: 2.530, 95% CI: 1.468-4.360, p < 0.001), MLR (HR: 2.229, 95% CI: 1.260-3.944, p = 0.006) and PLR (HR: 2.249, 95% CI: 1.300-3.889, p = 0.004) were connected to DFS. Multivariate Cox analysis showed that pN stage (HR: 3.098, 95% CI: 1.619-5.928, p < 0.001) was a separate DFS risk variable. CONCLUSION: The study demonstrates that NLR, MLR, and PLR play a convenient and cost-effective role in predicting survival and recurrence among individuals alongside central-type NSCLC having SL.

Individualized music induces theta-gamma phase-amplitude coupling in patients with disorders of consciousness.

Objective: This study aimed to determine whether patients with disorders of consciousness (DoC) could experience neural entrainment to individualized music, which explored the cross-modal influences of music on patients with DoC through phase-amplitude coupling (PAC). Furthermore, the study assessed the efficacy of individualized music or preferred music (PM) versus relaxing music (RM) in impacting patient outcomes, and examined the role of cross-modal influences in determining these outcomes. Methods: Thirty-two patients with DoC [17 with vegetative state/unresponsive wakefulness syndrome (VS/UWS) and 15 with minimally conscious state (MCS)], alongside 16 healthy controls (HCs), were recruited for this study. Neural activities in the frontal-parietal network were recorded using scalp electroencephalography (EEG) during baseline (BL), RM and PM. Cerebral-acoustic coherence (CACoh) was explored to investigate participants' abilitiy to track music, meanwhile, the phase-amplitude coupling (PAC) was utilized to evaluate the cross-modal influences of music. Three months post-intervention, the outcomes of patients with DoC were followed up using the Coma Recovery Scale-Revised (CRS-R). Results: HCs and patients with MCS showed higher CACoh compared to VS/UWS patients within musical pulse frequency (p = 0.016, p = 0.045; p < 0.001, p = 0.048, for RM and PM, respectively, following Bonferroni correction). Only theta-gamma PAC demonstrated a significant interaction effect between groups and music conditions (F (2,44) = 2.685, p = 0.036). For HCs, the theta-gamma PAC in the frontal-parietal network was stronger in the PM condition compared to the RM (p = 0.016) and BL condition (p < 0.001). For patients with MCS, the theta-gamma PAC was stronger in the PM than in the BL (p = 0.040), while no difference was observed among the three music conditions in patients with VS/UWS. Additionally, we found that MCS patients who showed improved outcomes after 3 months exhibited evident neural responses to preferred music (p = 0.019). Furthermore, the ratio of theta-gamma coupling changes in PM relative to BL could predict clinical outcomes in MCS patients (r = 0.992, p < 0.001). Conclusion: Individualized music may serve as a potential therapeutic method for patients with DoC through cross-modal influences, which rely on enhanced theta-gamma PAC within the consciousness-related network.

The ECG abnormalities in persons with chronic disorders of consciousness.

We aimed to investigate the electrocardiogram (ECG) features in persons with chronic disorders of consciousness (DOC, ≥ 29 days since injury, DSI) resulted from the most severe brain damages. The ECG data from 30 patients with chronic DOC and 18 healthy controls (HCs) were recorded during resting wakefulness state for about five minutes. The patients were classified into vegetative state (VS) and minimally conscious state (MCS). Eight ECG metrics were extracted for comparisons between the subject subgroups, and regression analysis of the metrics were conducted on the DSI (29-593 days). The DOC patients exhibit a significantly higher heart rate (HR, p = 0.009) and lower values for SDNN (p = 0.001), CVRR (p = 0.009), and T-wave amplitude (p < 0.001) compared to the HCs. However, there're no significant differences in QRS, QT, QTc, or ST amplitude between the two groups (p > 0.05). Three ECG metrics of the DOC patients-HR, SDNN, and CVRR-are significantly correlated with the DSI. The ECG abnormalities persist in chronic DOC patients. The abnormalities are mainly manifested in the rhythm features HR, SDNN and CVRR, but not the waveform features such as QRS width, QT, QTc, ST and T-wave amplitudes.

EEG power spectra parameterization and adaptive channel selection towards semi-supervised seizure prediction.

BACKGROUND: The seizure prediction algorithms have demonstrated their potential in mitigating epilepsy risks by detecting the pre-ictal state using ongoing electroencephalogram (EEG) signals. However, most of them require high-density EEG, which is burdensome to the patients for daily monitoring. Moreover, prevailing seizure models require extensive training with significant labeled data which is very time-consuming and demanding for the epileptologists. METHOD: To address these challenges, here we propose an adaptive channel selection strategy and a semi-supervised deep learning model respectively to reduce the number of EEG channels and to limit the amount of labeled data required for accurate seizure prediction. Our channel selection module is centered on features from EEG power spectra parameterization that precisely characterize the epileptic activities to identify the seizure-associated channels for each patient. The semi-supervised model integrates generative adversarial networks and bidirectional long short-term memory networks to enhance seizure prediction. RESULTS: Our approach is evaluated on the CHB-MIT and Siena epilepsy datasets. With utilizing only 4 channels, the method demonstrates outstanding performance with an AUC of 93.15% on the CHB-MIT dataset and an AUC of 88.98% on the Siena dataset. Experimental results also demonstrate that our selection approach reduces the model parameters and training time. CONCLUSIONS: Adaptive channel selection coupled with semi-supervised learning can offer the possible bases for a light weight and computationally efficient seizure prediction system, making the daily monitoring practical to improve patients' quality of life.

In vitro and in vivo stability of a highly efficient long-acting cocaine hydrolase.

It is recognized as a promising therapeutic strategy for cocaine use disorder to develop an efficient enzyme which can rapidly convert cocaine to physiologically inactive metabolites. We have designed and discovered a series of highly efficient cocaine hydrolases, including CocH5-Fc(M6) which is the currently known as the most efficient cocaine hydrolase with both the highest catalytic activity against (-)-cocaine and the longest biological half-life in rats. In the present study, we characterized the time courses of protein appearance, pH, structural integrity, and catalytic activity against cocaine in vitro and in vivo of a CocH5-Fc(M6) bulk drug substance produced in a bioreactor for its in vitro and in vivo stability after long-time storage under various temperatures (- 80, - 20, 4, 25, or 37 °C). Specifically, all the tested properties of the CocH5-Fc(M6) protein did not significantly change after the protein was stored at any of four temperatures including - 80, - 20, 4, and 25 °C for ~ 18 months. In comparison, at 37 °C, the protein was less stable, with a half-life of ~ 82 days for cocaine hydrolysis activity. Additionally, the in vivo studies further confirmed the linear elimination PK profile of CocH5-Fc(M6) with an elimination half-life of ~ 9 days. All the in vitro and in vivo data on the efficacy and stability of CocH5-Fc(M6) have consistently demonstrated that CocH5-Fc(M6) has the desired in vitro and in vivo stability as a promising therapeutic candidate for treatment of cocaine use disorder.

Unraveling CCL20's role by regulating Th17 cell chemotaxis in experimental autoimmune prostatitis.

Chronic prostatitis and chronic pelvic pain syndrome (CP/CPPS), a prevalent urological ailment, exerts a profound influence upon the well-being of the males. Autoimmunity driven by Th17 cells has been postulated as a potential factor in CP/CPPS pathogenesis. Nonetheless, elucidating the precise mechanisms governing Th17 cell recruitment to the prostate, triggering inflammation, remained an urgent inquiry. This study illuminated that CCL20 played a pivotal role in attracting Th17 cells to the prostate, thereby contributing to prostatitis development. Furthermore, it identified prostate stromal cells and immune cells as likely sources of CCL20. Additionally, this research unveiled that IL-17A, released by Th17 cells, could stimulate macrophages to produce CCL20 through the NF-κB/MAPK/PI3K pathway. The interplay between IL-17A and CCL20 establishes a positive feedback loop, which might serve as a critical mechanism underpinning the development of chronic prostatitis, thus adding complexity to its treatment challenges.

Alpha anteriorization and theta posteriorization during deep sleep.

Brain states (wake, sleep, general anesthesia, etc.) are profoundly associated with the spatiotemporal dynamics of brain oscillations. Previous studies showed that the EEG alpha power shifted from the occipital cortex to the frontal cortex (alpha anteriorization) after being induced into a state of general anesthesia via propofol. The sleep research literature suggests that slow waves and sleep spindles are generated locally and propagated gradually to different brain regions. Since sleep and general anesthesia are conceptualized under the same framework of consciousness, the present study examines whether alpha anteriorization similarly occurs during sleep and how the EEG power in other frequency bands changes during different sleep stages. The results from the analysis of three polysomnography datasets of 234 participants show consistent alpha anteriorization during the sleep stages N2 and N3, beta anteriorization during stage REM, and theta posteriorization during stages N2 and N3. Although it is known that the neural circuits responsible for sleep are not exactly the same for general anesthesia, the findings of alpha anteriorization in this study suggest that, at macro level, the circuits for alpha oscillations are organized in the similar cortical areas. The spatial shifts of EEG power in different frequency bands during sleep may offer meaningful neurophysiological markers for the level of consciousness.

[Quality control of Tianwang Buxin Pills based on UPLC fingerprint and multi-component quantification].

Tianwang Buxin Pills have demonstrated therapeutic effects in clinical practice, whereas there is a serious lack of comprehensive quality control to ensure the safety and effectiveness of clinical medication. In this study, ultra-performance liquid chromatography(UPLC) was employed to establish the fingerprint and the method for simultaneously determining the content of seven components of Tianwang Buxin Pills. Furthermore, chemometrics was employed to identify the key factors for the stable quality, which provided a reference for the comprehensive quality control and evaluation of this preparation. There were 25 common peaks in the UPLC fingerprints of 15 batches of Tianwang Buxin Pills, from which thirteen compounds were identified. A quantitation method was established for seven pharmacological components(α-linolenic acid, salvianolic acid B, glycyrrhetinic acid, schisandrin A, ß-asarone, 3,6'-disinapoylsucrose, and ligustilide). The principal component analysis(PCA) and partial least square discriminate analysis(PLS-DA) were performed to determine the key pharmacological components for controlling the quality stability of Tianwang Buxin Pills, which included 3,6'-disinapoylsucrose, α-linolenic acid, and ß-asarone. The established fingerprint and multi-component content determination method have strong specificity, stability, and reliability. In addition, 3,6'-disinapoylsucrose, α-linolenic acid, and ß-asarone are the key pharmacological components that ensure the quality stability between batches and can be used to comprehensively control the quality of Tianwang Buxin Pills. The findings provide a scientific basis for the quality evaluation and standard establishment of Tianwang Buxin Pills.

A highly selective mPGES-1 inhibitor to block abdominal aortic aneurysm progression in the angiotensin mouse model.

Abdominal aortic aneurysm (AAA) is a deadly, permanent ballooning of the aortic artery. Pharmacological and genetic studies have pointed to multiple proteins, including microsomal prostaglandin E2 synthase-1 (mPGES-1), as potentially promising targets. However, it remains unknown whether administration of an mPGES-1 inhibitor can effectively attenuate AAA progression in animal models. There are still no FDA-approved pharmacological treatments for AAA. Current research stresses the importance of both anti-inflammatory drug targets and rigor of translatability. Notably, mPGES-1 is an inducible enzyme responsible for overproduction of prostaglandin E2 (PGE2)-a well-known principal pro-inflammatory prostanoid. Here we demonstrate for the first time that a highly selective mPGES-1 inhibitor (UK4b) can completely block further growth of AAA in the ApoE-/- angiotensin (Ang)II mouse model. Our findings show promise for the use of a mPGES-1 inhibitor like UK4b as interventional treatment of AAA and its potential translation into the clinical setting.

Long-lasting blocking of interoceptive effects of cocaine by a highly efficient cocaine hydrolase in rats.

Cocaine dependence is a serious world-wide public health problem without an FDA-approved pharmacotherapy. We recently designed and discovered a highly efficient long-acting cocaine hydrolase CocH5-Fc(M6). The present study examined the effectiveness and duration of CocH5-Fc(M6) in blocking interoceptive effects of cocaine by performing cocaine discrimination tests in rats, demonstrating that the duration of CocH5-Fc(M6) in blocking cocaine discrimination was dependent on cocaine dose and CocH5-Fc(M6) plasma concentration. Particularly, a dose of 3 mg/kg CocH5-Fc(M6) effectively attenuated discriminative stimulus effects of 10 mg/kg cocaine, cumulative doses of 10 and 32 mg/kg cocaine, and cumulative doses of 10, 32 and 56 mg/kg cocaine by ≥ 20% for 41, 19, and 10 days, and completely blocked the discriminative stimulus effects for 30, 13, and 5 days with corresponding threshold plasma CocH5-Fc(M6) concentrations of 15.9, 72.2, and 221 nM, respectively, under which blood cocaine concentration was negligible. Additionally, based on the data obtained, cocaine discrimination model is more sensitive than the locomotor activity to reveal cocaine effects and that CocH5-Fc(M6) itself has no long-term toxicity regarding behavioral activities such as lever pressing and food consumption in rats, further demonstrating that CocH5-Fc(M6) has the desired properties as a promising therapeutic candidate for prevenance of cocaine dependence.

Unraveling the allosteric mechanisms of prolyl endopeptidases for celiac disease therapy: Insights from molecular dynamics simulations.

Prolyl endopeptidases (PEP) from Sphingomonas capsulata (sc) and Myxococcus xanthus (mx) selectively degrade gluten peptides in vitro, offering a potential therapeutic strategy for celiac disease. However, the mechanisms governing the interaction of these enzymes with their substrates remain unclear. In this study, conventional molecular dynamics simulations with a microsecond timescale and targeted molecular dynamics simulations were performed to investigate the native states of mxPEP and scPEP enzymes, as well as their allosteric binding with a representative substrate, namely, Z-Ala-Pro-p-nitroanilide (pNA). The simulations reveal that the native scPEP is in an open state, while the native mxPEP is in a closed state. When pNA approaches a closed mxPEP, it binds to an allosteric pocket located at the first and second ß-sheet of the ß-propeller domain, inducing the opening of this enzyme. Neither enzyme is active in the open or partly-open states. Enzymatic activity is enabled only when the catalytic pocket in the closed state fully accommodates the substrates. The internal capacity of the catalytic pocket of PEP in the closed state determines the maximum size of the gluten peptides that the enzymes can catalyze. The present work provides essential molecular dynamics information for the redesign or engineering of PEP enzymes.

Reexamining the effects of drug loading on the in vivo performance of PEGylated liposomal doxorubicin.

Higher drug loading employed in nanoscale delivery platforms is a goal that researchers have long sought after. But such viewpoint remains controversial because the impacts that nanocarriers bring about on bodies have been seriously overlooked. In the present study we investigated the effects of drug loading on the in vivo performance of PEGylated liposomal doxorubicin (PLD). We prepared PLDs with two different drug loading rates: high drug loading rate, H-Dox, 12.9% w/w Dox/HSPC; low drug loading rate, L-Dox, 2.4% w/w Dox/HSPC (L-Dox had about 5 folds drug carriers of H-Dox at the same Dox dose). The pharmaceutical properties and biological effects of H-Dox and L-Dox were compared in mice, rats or 4T1 subcutaneous tumor-bearing mice. We showed that the lowering of doxorubicin loading did not cause substantial shifts to the pharmaceutical properties of PLDs such as in vitro and in vivo stability (stable), anti-tumor effect (equivalent effective), as well as tissue and cellular distribution. Moreover, it was even more beneficial for mitigating the undesired biological effects caused by PLDs, through prolonging blood circulation and alleviating cutaneous accumulation in the presence of pre-existing anti-PEG Abs due to less opsonins (e.g. IgM and C3) deposition on per particle. Our results warn that the effects of drug loading would be much more convoluted than expected due to the complex intermediation between nanocarriers and bodies, urging independent investigation for each individual delivery platform to facilitate clinical translation and application.

Parameterized aperiodic and periodic components of single-channel EEG enables reliable seizure detection.

Although it is clinically important, a reliable and economical solution to automatic seizure detection for patients at home is yet to be developed. Traditional algorithms rely on multi-channel EEG signals and features of canonical EEG power description. This study is aimed to propose an effective single-channel EEG seizure detection method centered on novel EEG power parameterization and channel selection algorithms. We employed the publicly available multi-channel CHB-MIT Scalp EEG database to gauge the effectiveness of our approach. We first adapted a power spectra parameterization algorithm to characterize the aperiodic and periodic components of the ictal and inter-ictal EEGs. We selected four features based on their statistical significance and interpretability, and developed a ranking approach to channel selection for each patient. We then tested the effectiveness of our approaches to channel and feature selection for automatic seizure detection using support vector machine (SVM) as the classifier. The performance of our algorithm was evaluated using five-fold cross-validation and compared to those methods of comparable complexity (using one or two channels of EEG), in terms of accuracy, specificity, sensitivity, precision and F1 score. Some channels of EEG signals show strikingly different distributions of PSD features between the ictal and inter-ictal states. Four features including the offset and exponent parameters for the aperiodic component and the first and second highest total power (TPW1 and TPW2) form the basis of channel selection and the input of SVM classifier. The selected channel is found to be patient-specific. Our approach has achieved a mean sensitivity of 95.6%, specificity of 99.2%, accuracy of 98.6%, precision of 95.5%, and F1 score of 95.5%. Compared with algorithms in previous studies that used one or two channels of EEG signals, ours outperforms in specificity and accuracy with comparable sensitivity. EEG power spectra parameterization to feature extraction and feature ranking-based channel selection are found to enable efficient and effective automatic seizure detection based on single-channel EEG signal.

Microglia as a Game Changer in Epilepsy Comorbid Depression.

As one of the most common neurological diseases, epilepsy is often accompanied by psychiatric disorders. Depression is the most universal comorbidity of epilepsy, especially in temporal lobe epilepsy (TLE). Therefore, it is urgently needed to figure out potential mechanisms and the optimization of therapeutic strategies. Microglia play a pivotal role in the coexistent relationship between epilepsy and depression. Activated microglia released cytokines like IL-6 and IL-1ß, orchestrating neuroinflammation especially in the hippocampus, worsening both depression and epilepsy. The decrease of intracellular K+ is a common part in various molecular changes. The P2X7-NLRP3-IL-1ß is a major inflammatory pathway that disrupts brain network. Extra ATP and CX3CL1 also lead to neuronal excitotoxicity and blood-brain barrier (BBB) disruption. Regulating neuroinflammation aiming at microglia-related molecules is capable of suspending the vicious mutual aggravating circle of epilepsy and depression. Other overlaps between epilepsy and depression lie in transcriptomic, neuroimaging, diagnosis and treatment. Hippocampal sclerosis (HS) and amygdala enlargement (AE) may be the underlying macroscopic pathological changes according to current studies. Extant evidence shows that cognitive behavioral therapy (CBT) and antidepressants like selective serotonin-reuptake inhibitors (SSRIs) are safe, but the effect is limited. Improvement in depression is likely to reduce the frequency of seizure. More comprehensive experiments are warranted to better understand the relationship between them.

Human Butyrylcholinesterase Mutants for (-)-Cocaine Hydrolysis: A Correlation Relationship between Catalytic Efficiency and Total Hydrogen Bonding Energy with an Oxyanion Hole.

A combined computational and experimental study has been carried out to explore and test a quantitative correlation relationship between the relative catalytic efficiency (RCE) of human butyrylcholinesrase (BChE) mutant-catalyzed hydrolysis of substrate (-)-cocaine and the total hydrogen bonding energy (tHBE) of the carbonyl oxygen of the substrate with the oxyanion hole of the enzyme in the modeled transition-state structure (TS1), demonstrating a satisfactory linear correlation relationship between ln(RCE) and tHBE. The satisfactory correlation relationship has led us to computationally predict and experimentally confirm new human BChE mutants that have a further improved catalytic activity against (-)-cocaine, including the most active one (the A199S/F227S/S287G/A328W/Y332G mutant) with a 2790-fold improved catalytic efficiency (kcat/KM = 2.5 × 109 min-1 M-1) compared to the wild-type human BChE. Compared to the reference mutant (the A199S/S287G/A328W/Y332G mutant) tested in the reported clinical development of an enzyme therapy for cocaine dependence treatment, this new mutant (with a newly predicted additional F227S mutation) has an improved catalytic efficiency against (-)-cocaine by ∼2.6-fold. The good agreement between the computational and experimental ln(RCE) values suggests that the obtained correlation relationship is robust for computational prediction. A similar correlation relationship could also be explored in studying BChE or other serine hydrolases/esterases with an oxyanion hole stabilizing the carbonyl oxygen in the rate-determining reaction step of the enzymatic hydrolysis of other substrates.

Multiobjective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.

Opioid use disorder (OUD) has emerged as a significant global public health issue, necessitating the discovery of new medications. In this study, we propose a deep generative model that combines a stochastic differential equation (SDE)-based diffusion model with a pretrained autoencoder. The molecular generator enables efficient generation of molecules that target multiple opioid receptors, including mu, kappa, and delta. Additionally, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the generated molecules to identify druglike compounds. We develop a molecular optimization approach to enhance the pharmacokinetic properties of some lead compounds. Advanced binding affinity predictors were built using molecular fingerprints, including autoencoder embeddings, transformer embeddings, and topological Laplacians. Our process yields druglike molecules that can be used in highly focused experimental studies to further evaluate their pharmacological effects. Our machine learning platform serves as a valuable tool for designing effective molecules to address OUD.

Multi-objective Molecular Optimization for Opioid Use Disorder Treatment Using Generative Network Complex.

Opioid Use Disorder (OUD) has emerged as a significant global public health issue, with complex multifaceted conditions. Due to the lack of effective treatment options for various conditions, there is a pressing need for the discovery of new medications. In this study, we propose a deep generative model that combines a stochastic differential equation (SDE)-based diffusion modeling with the latent space of a pretrained autoencoder model. The molecular generator enables efficient generation of molecules that are effective on multiple targets, specifically the mu, kappa, and delta opioid receptors. Furthermore, we assess the ADMET (absorption, distribution, metabolism, excretion, and toxicity) properties of the generated molecules to identify drug-like compounds. To enhance the pharmacokinetic properties of some lead compounds, we employ a molecular optimization approach. We obtain a diverse set of drug-like molecules. We construct binding affinity predictors by integrating molecular fingerprints derived from autoencoder embeddings, transformer embeddings, and topological Laplacians with advanced machine learning algorithms. Further experimental studies are needed to evaluate the pharmacological effects of these drug-like compounds for OUD treatment. Our machine learning platform serves as a valuable tool in designing and optimizing effective molecules for addressing OUD.

Staying Ahead of the Game: How SARS-CoV-2 has Accelerated the Application of Machine Learning in Pandemic Management.

In recent years, machine learning (ML) techniques have garnered considerable interest for their potential use in accelerating the rate of drug discovery. With the emergence of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the utilization of ML has become even more crucial in the search for effective antiviral medications. The pandemic has presented the scientific community with a unique challenge, and the rapid identification of potential treatments has become an urgent priority. Researchers have been able to accelerate the process of identifying drug candidates, repurposing existing drugs, and designing new compounds with desirable properties using machine learning in drug discovery. To train predictive models, ML techniques in drug discovery rely on the analysis of large datasets, including both experimental and clinical data. These models can be used to predict the biological activities, potential side effects, and interactions with specific target proteins of drug candidates. This strategy has proven to be an effective method for identifying potential coronavirus disease 2019 (COVID-19) and other disease treatments. This paper offers a thorough analysis of the various ML techniques implemented to combat COVID-19, including supervised and unsupervised learning, deep learning, and natural language processing. The paper discusses the impact of these techniques on pandemic drug development, including the identification of potential treatments, the understanding of the disease mechanism, and the creation of effective and safe therapeutics. The lessons learned can be applied to future outbreaks and drug discovery initiatives.

Characterization of 2 Novel Phosphodiesterase 2 Inhibitors Hcyb1 and PF-05180999 on Depression- and Anxiety-Like Behavior.

BACKGROUND: Phosphodiesterase 2A (PDE2A) represents a novel target for new therapies addressing psychiatric disorders. To date, the development of PDE2A inhibitors suitable for human clinical evaluation has been hampered by the poor brain accessibility and metabolic stability of the available compounds. METHODS: Corticosterone (CORT)-induced neuronal cell lesion and restraint stress mouse model were used to measure the neuroprotective effect in cells and antidepressant-like behavior in mice. RESULTS: The cell-based assay showed that both Hcyb1 and PF were potent in protecting cells against stress hormone CORT insults by stimulating cAMP and cGMP signaling in hippocampal cells (HT-22). Administration of both compounds before treatment of CORT to cells increased cAMP/cGMP, VASP phosphorylation at Ser239 and Ser157, cAMP response element binding protein phosphorylation at Ser133, and brain derived neurotrophic factor BDNF expression. Further in vivo study showed that both Hcyb1 and PF displayed -antidepressant- and anxiolytic-like effects against restraint stress as indicated by reduced immobility time in the forced swimming and tail suspension tasks as well as increased open arm entries and time spent in open arms and holes visit in elevated plus maze and hole-board tests, respectively. The biochemical study confirmed that these antidepressant- and anxiolytic-like effects of Hcyb1 and PF were related to cAMP and cGMP signaling in the hippocampus. CONCLUSIONS: The results extend the previous studies and validate that PDE2A is a tractable target for drug development in the treatment of emotional disorders such as depression and anxiety.

Stone attenuation on computer tomography helps surgeons make decisions between miniaturized percutaneous nephrolithotomy or retrograde intrarenal surgery for lower pole stones: a retrospective study.

A retrospective study was performed on 200 patients who underwent miniaturized percutaneous nephrolithotomy (mini-PCNL) or retrograde intrarenal surgery (RIRS) for 10-20 mm sized lower pole renal calculi to investigate the relationship between computed tomography (CT) attenuation of calculi and surgical outcomes. CT was used to examine the location, size, and CT attenuation values of the calculi. Additionally, the operation time, hospital stay, hemoglobin (Hb) reduction, stone-free rate (SFR), and complication rate were also meticulously documented and subjected to comparative analysis. Complications were assessed using the Clavien-Dindo grading system. We observed no significant differences in hospitalization data and follow-up outcomes, except for a longer hospital stay and higher Hb drops in patients receiving mini-PCNL. Statistical analysis revealed an association between CT attenuation and operation time. Compared with mini-PCNL, RIRS could reduce bleeding, hospital stay, surgery time, and complications for 10-20 mm sized lower pole kidney stones with CT values < 1000 HU. RIRS resulted in longer operation time and lower stone-free rates despite shorter hospital stays and less bleeding than mini-PCNL for stones with CT values > 1000 HU. Therefore, selecting an appropriate surgical method based on CT attenuation might improve outcomes. For patients with stone attenuation values < 1000 HU, RIRS is the recommended option. When stone attenuation values > 1000 HU, the surgical method should be chosen based on the patient's individual situation.