Receive a weekly summary and discussion of the top papers of the week by leading researchers in the field.

General General

Effective degradation of tetracycline via recyclable free-standing three-dimensional copper-based graphene as a persulfate catalyst.

In Environmental science and pollution research international

Water pollution by antibiotics is a serious and growing problem. Given this challenge, a free-standing three-dimensional (3D) reduced graphene oxide foam supported copper oxide nanoparticles (3D-rGO-CuxO) was synthesized using GO as a precursor and applied as an efficient persulfate activator for tetracycline (TC) degradation. The influences of CuxO mass, solution pH, persulfate dosage, and common anions on the TC degradation were investigated in detail. Analytical techniques indicated that the 3D-rGO-CuxO showed a cross-linking three-dimensional network structure, and CuxO particles with irregular shapes were uniformly loaded on graphene pore walls. The XPS and Auger spectra of Cu confirmed that Cu2O was the main component in solid copper compounds. The addition of CuxO was vitally important for the activation of the oxidation system, and the removal rate reached 98% with a CuxO load of 7:1. The pH showed little influence on the activation effect on TC degradation. For common anions, Cl- and CO32- had little influence on the system, while humic acid had a great inhibitory effect. The EPR test and quenching experiment revealed that the active substances in the oxidative degradation process mainly include SO4-·, ·OH, 1O2, and reactive Cu(III). Additionally, the 3D-rGO-CuxO material proved highly stable according to the replicated test results and was promising for the remediation of antibiotic-contaminated water.

Zhao Chuanqi, Liang Liying, Shi Qin, Xia Hui, Li Chaofan, Ma Junguan

2023-Mar-21

3D-rGO-CuxO, Active radicals, Degradation, Persulfate activation, Tetracycline

General General

Artificial intelligence based virtual screening study for competitive and allosteric inhibitors of the SARS-CoV-2 main protease.

In Journal of biomolecular structure & dynamics

SARS-CoV-2 is a highly contagious and dangerous coronavirus that first appeared in late 2019 causing COVID-19, a pandemic of acute respiratory illnesses that is still a threat to health and the general public safety. We performed deep docking studies of 800 M unique compounds in both the active and allosteric sites of the SARS-COV-2 Main Protease (Mpro) and the 15 M and 13 M virtual hits obtained were further taken for conventional docking and molecular dynamic (MD) studies. The best XP Glide docking scores obtained were -14.242 and -12.059 kcal/mol by CHEMBL591669 and the highest binding affinities were -10.5 kcal/mol (from 444215) and -11.2 kcal/mol (from NPC95421) for active and allosteric sites, respectively. Some hits can bind both sites making them a great area of concern. Re-docking of 8 random allosteric complexes in the active site shows a significant reduction in docking scores with a t-test P value of 2.532 × 10-11 at 95% confidence. Some specific interactions have higher elevations in docking scores. MD studies on 15 complexes show that single-ligand systems are stable as compared to double-ligand systems, and the allosteric binders identified are shown to modulate the active site binding as evidenced by the changes in the interaction patterns and stability of ligands and active site residues. When an allosteric complex was docked to the second monomer to check for homodimer formation, the validated homodimer could not be re-established, further supporting the potential of the identified allosteric binders. These findings could be important in developing novel therapeutics against SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Charles Ssemuyiga, Edgar Mulumba Pius, Mahapatra Rajani Kanta

2023-Mar-21

Artificial intelligence, COVID-19, SARS-CoV-2 main protease, deep docking, molecular docking, molecular dynamics simulation, neural networks

Surgery Surgery

Nanotechnology and Artificial Intelligence: An Emerging Paradigm for Postoperative Patient Care.

In Aesthetic surgery journal ; h5-index 35.0

BACKGROUND : An increasing number of aesthetic surgery procedures are being performed in the office-based setting in an ambulatory fashion. Postoperative monitoring for these patients has historically been comprised of paid private-duty nurses measuring vital signs, encouraging ambulation, and monitoring overall comfort level. Recently, advancements in nanotechnology have permitted high-acuity data acquisition of multiple clinical parameters that can be transmitted to the surgeon's mobile device in a continuous fashion.

OBJECTIVE : To describe the authors early experience with this emerging artificial intelligence technology in the postoperative setting.

METHODS : Twenty-three consecutive patients underwent radiofrequency-assisted liposuction and Brazilian Butt Lift (BBL) surgery and placed in a monitoring garment postoperatively. The primary outcome was device usability, reflected by compliance with device and completeness of data collection.

RESULTS : Ninety-one percent of patients wore the device for greater than 12 hours a day in the first 48 hours. Only 39% were compliant with postoperative positioning. No postoperative events were detected.

CONCLUSIONS : The quality of data collected allow for detection of clinical derangements and can alert the surgeon in real time, prompting intervention such as medicine administration, position changes or presentation to the emergency room.

Del Vecchio Daniel, Stein Michael J, Dayan Erez, Marte Joseph, Theodorou Spero

2023-Mar-21

General General

Development and validation of machine learning-based model for mortality prediction in patients with acute basilar artery occlusion receiving endovascular treatment: multicentric cohort analysis.

In Journal of neurointerventional surgery ; h5-index 49.0

BACKGROUND : Predicting mortality in stroke patients using information available before endovascular treatment (EVT) is an essential component for supporting clinical decision-making. Although the mortality rate of acute basilar artery occlusion (ABAO) after EVT has reached 40%, few studies have focused on predicting mortality in these individuals. Thus, we aimed to develop and validate a machine learning-based mortality prediction tool based on preoperative information for ABAO patients receiving EVT.

METHODS : The derivation cohort comprised patients from southern provinces of China in the BASILAR registry. The model (POSITIVE: Predicting mOrtality of baSilar artery occlusion patIents Treated wIth EVT) was trained and optimized using a fivefold cross-validation method in which hyperparameters were selected and fine-tuned. This model was retrospectively tested in patients from the northern provinces of China from the BASILAR registry. A prospective test of POSITIVE was performed on consecutive patients from two hospitals between January 2020 and June 2022.

RESULTS : Extreme gradient boosting was employed to construct the POSITIVE model, which achieved the best predictive performance among the eight machine learning algorithms and showed excellent discrimination (area under the curve (AUC) 0.83, 95% confidence interval (95% CI) 0.80 to 0.87) and calibration (Hosmer-Lemeshow test, P>0.05) in the development cohort. AUC yielded by the POSITIVE model for the retrospective test was 0.79 (95% CI 0.71 to 0.85), higher than that obtained by traditional models. Prospective comparisons showed that the POSITIVE model achieved the highest AUC (0.82, 95% CI 0.74 to 0.90) among all prediction models.

CONCLUSION : We developed a machine learning algorithm and retrospective and prospective testing with multicentric cohorts, which exhibited a solid predictive performance and may act as a convenient reference to guide decision-making for ABAO patients. The POSITIVE model is presented online for user-friendly access.

Liu Chang, Huang Jiacheng, Kong Weilin, Chen Liyuan, Song Jiaxing, Yang Jie, Li Fengli, Zi Wenjie

2023-Mar-21

Intervention, Stroke

General General

Filopodia-like protrusions of adjacent somatic cells shape the developmental potential of oocytes.

In Life science alliance

The oocyte must grow and mature before fertilization, thanks to a close dialogue with the somatic cells that surround it. Part of this communication is through filopodia-like protrusions, called transzonal projections (TZPs), sent by the somatic cells to the oocyte membrane. To investigate the contribution of TZPs to oocyte quality, we impaired their structure by generating a full knockout mouse of the TZP structural component myosin-X (MYO10). Using spinning disk and super-resolution microscopy combined with a machine-learning approach to phenotype oocyte morphology, we show that the lack of Myo10 decreases TZP density during oocyte growth. Reduction in TZPs does not prevent oocyte growth but impairs oocyte-matrix integrity. Importantly, we reveal by transcriptomic analysis that gene expression is altered in TZP-deprived oocytes and that oocyte maturation and subsequent early embryonic development are partially affected, effectively reducing mouse fertility. We propose that TZPs play a role in the structural integrity of the germline-somatic complex, which is essential for regulating gene expression in the oocyte and thus its developmental potential.

Crozet Flora, Letort Gaëlle, Bulteau Rose, Da Silva Christelle, Eichmuller Adrien, Tortorelli Anna Francesca, Blévinal Joséphine, Belle Morgane, Dumont Julien, Piolot Tristan, Dauphin Aurélien, Coulpier Fanny, Chédotal Alain, Maître Jean-Léon, Verlhac Marie-Hélène, Clarke Hugh J, Terret Marie-Emilie

2023-Jun

General General

Forecasting Risk of Future Rapid Glaucoma Worsening Using Early Visual Field, Optical Coherence Tomography, and Clinical Data.

In Ophthalmology. Glaucoma

PURPOSE : Assess whether we can forecast future rapid visual field (VF) worsening using deep learning models (DLMs) trained on early VF, OCT, and clinical data.

DESIGN : Retrospective cohort study.

SUBJECTS : 4,536 eyes from 2,962 patients. 263 (5.80%) of eyes underwent rapid VF worsening (MD slope <-1dB/yr across all VFs).

METHODS : We included eyes that met the following criteria: 1) followed for glaucoma or suspect status 2) had at least five longitudinal reliable VFs (VF1, VF2, VF3, VF4, VF5) 3) had one reliable baseline Optical Coherence Tomography (OCT) scan (OCT1) and one set of baseline clinical measurements (Clinical1) at the time of VF1. We designed a DLM to forecast future rapid VF worsening. The input consisted of spatially oriented total deviation values from VF1 (including or not including VF2 and VF3 in some models) and retinal nerve fiber layer thickness values from the baseline OCT. We passed this VF/OCT stack into a vision transformer feature extractor, the output of which was concatenated with baseline clinical data before putting it through a linear classifier to predict that eye's risk of rapid VF worsening across the five VFs. We compared the performance of models with differing inputs by computing area under receiver operating curve (AUC) in the test set. Specifically, we trained models with the following inputs: Model V: VF1; VC: VF1+ Clinical1; VO: VF1+ OCT1; VOC: VF1+ Clinical1+ OCT1; V2: VF1 + VF2; V2OC: VF1 + VF2 + Clinical1 + OCT1; V3: VF1 + VF2 + VF3; V3OC: VF1 + VF2 + VF3 + Clinical1 + OCT1.

MAIN OUTCOME MEASURES : AUC of DLMs when forecasting rapidly worsening eyes.

RESULTS : Model V3OC best forecasted rapid worsening with an AUC (95% CI) of 0.87 (0.77, 0.97). Remaining models in descending order of performance and their respective AUC [95% CI] were: Model V3 (0.84 [0.74 to 0.95]), Model V2OC (0.81 [0.70 to 0.92]), Model V2 (0.81 [0.70 to 0.82]), Model VOC (0.77 [0.65, 0.88]), Model VO [0.75 [0.64, 0.88], Model VC (0.75 [0.63, 0.87]), Model V (0.74 [0.62, 0.86]).

CONCLUSION : DLMs can forecast future rapid glaucoma worsening with modest to high performance when trained using data from early in the disease course. Including baseline data from multiple modalities and subsequent visits improves performance beyond using VF data alone.

Herbert Patrick, Hou Kaihua, Bradley Chris, Hager Greg, Boland Michael V, Ramulu Pradeep, Unberath Mathias, Yohannan Jithin

2023-Mar-19

Deep Learning, Forecasting, Glaucoma