Risk Factors for Wound Infections after Vascular Surgery: Kuwait Experience

IntroductionWound infections represent a serious complication after vascular surgery particularly after vascular reconstructive procedures. We aimed to identify risk factors predisposing patients to these complications.MethodsThis was a retrospective review of open vascular surgical procedures performed between April 2014 and March 2019 in Kuwait. Patient demographics, procedures performed and their indications, and post-operative outcomes were collected and analyzed. Patients with pre-operative active infections were excluded from the analysis. Statistical analysis was performed, and odds ratios (ORs) and relative risks were calculated for the outcomes of interest. Fisher''s exact test and two-tailed t test were used where appropriate.Results391 patients were identified. The majority (54%) presented with chronic limb threatening ischemia. The mean age was 58 (±10) years, with a male predominance (76%). Wound infection occurred in 53 (14%) patients. The most commonly isolated organism was Staphylococcus aureus (47%). Diabetes (OR 8.03, 95% CI: 1.9142–33.7439, p = 0.0044), hypertension (OR 2.38, 95% CI: 1.2960–4.3684, p = 0.0052), ischemic heart disease (OR 2.30, 95% CI: 1.4349–4.6987, p = 0.0016), hyperlipidemia (OR 2.12, 95% CI: 1.0305–4.3620, p = 0.0412), and chronic renal failure (OR 2.55, 95% CI: 1.0181–6.4115, p = 0.0457) were all found to be significantly associated with the development of post-operative wound infections in vascular surgery patients.ConclusionDiabetes, hypertension, ischemic heart disease, hyperlipidemia, and chronic renal failure were associated with post-operative wound infections. Anticipation of wound complications in patients with these risk factors may aid early diagnosis and treatment.     

Forecasting the Post-Pandemic Effects of the SARS-CoV-2 Virus Using the Bullwhip Phenomenon Alongside Use of Nanosensors for Disease Containment and Cure

The COVID-19 pandemic has the tendency to affect various organizational paradigm alterations, which civilization hasyet to fully comprehend. Personal to professional, individual to corporate, and across most industries, the spectrum of transformations is vast. Economically, the globe has never been more intertwined, and it has never been subjected to such widespread disruption. While many people have felt and acknowledged the pandemic’s short-term repercussions, the resultant paradigm alterations will certainly have long-term consequences with an unknown range and severity. This review paper aims at acknowledging various approaches for the prevention, detection, and diagnosis of the SARS-CoV-2 virus using nanomaterials as a base material. A nanostructure is a material classification based on dimensionality, in proportion to the characteristic diameter and surface area. Nanoparticles, quantum dots, nanowires (NW), carbon nanotubes (CNT), thin films, and nanocomposites are some examples of various dimensions, each acting as a single unit, in terms of transport capacities. Top-down and bottom-up techniques are used to fabricate nanomaterials. The large surface-to-volume ratio of nanomaterials allows one to create extremely sensitive charge or field sensors (electrical sensors, chemical sensors, explosives detection, optical sensors, and gas sensing applications). Nanowires have potential applications in information and communication technologies, low-energy lightning, and medical sensors. Carbon nanotubes have the best environmental stability, electrical characteristics, and surface-to-volume ratio of any nanomaterial, making them ideal for bio-sensing applications. Traditional commercially available techniques have focused on clinical manifestations, as well as molecular and serological detection equipment that can identify the SARS-CoV-2 virus. Scientists are expressing a lot of interest in developing a portable and easy-to-use COVID-19 detection tool. Several unique methodologies and approaches are being investigated as feasible advanced systems capable of meeting the demands. This review article attempts to emphasize the pandemic’s aftereffects, utilising the notion of the bullwhip phenomenon’s short-term and long-term effects, and it specifies the use of nanomaterials and nanosensors for detection, prevention, diagnosis, and therapy in connection to the SARS-CoV-2.     

Predicting Compressive and Splitting Tensile Strengths of Silica Fume Concrete Using M5P Model Tree Algorithm

Compressive strength (CS) and splitting tensile strength (STS) are paramount parameters in the design of reinforced concrete structures and are required by pertinent standard provisions. Robust prediction models for these properties can save time and cost by reducing the number of laboratory trial batches and experiments needed to generate suitable design data. Silica fume (SF) is often used in concrete owing to its substantial enhancements of the engineering properties of concrete and its environmental benefits. In the present study, the M5P model tree algorithm was used to develop models for the prediction of the CS and STS of concrete incorporating SF. Accordingly, large databases comprising 796 data points for CS and 156 data records for STS were compiled from peer-reviewed published literature. The predictions of the M5P models were compared with linear regression analysis and gene expression programming. Different statistical metrics, including the coefficient of determination, correlation coefficient, root mean squared error, mean absolute error, relative squared error, and discrepancy ratio, were deployed to appraise the performance of the developed models. Moreover, parametric analysis was carried out to investigate the influence of different input parameters, such as the SF content, water-to-binder ratio, and age of the specimen, on the CS and STS. The trained models offer a rapid and accurate tool that can assist the designer in the effective proportioning of silica fume concrete.     

Whole exome sequencing identified a novel single base pair insertion mutation in the EYS gene in a six generation family with retinitis pigmentosa

Retinitis pigmentosa (RP) is a group of inherited progressive retinal dystrophies (RD) and is characterized by photoreceptor degeneration. RP is clinically and genetically heterogeneous disorder. More than 70 genes are known and, thus, identification of causative genes and mutations in known genes is challenging. This study was designed to identify the underlying genetic defect in a large extended Saudi family with multiple RP affected members. Fundus photography, Optical Coherence Tomography (OCT) and visual field perimetry were performed for affected individuals. Whole exome sequencing was used to detect the underlying genetic defect in a large family with 12 affected individuals showing autosomal recessive isolated RP. WES data analysis identified a novel insertion mutation in the EYS (eyes shut homolog) gene (c.910_911insT; p.Trp304LeufsTer8). Sanger sequencing validates the variant discovered through exome in all 12 affected individuals and showed that this mutation is segregating with RP phenotype in an autosomal recessive manner in 51 individuals of the family tested here. Our study expands the mutation spectrum of EYS gene in RP patients and extends the body of evidence that supports the importance of EYS gene in eye development.     

Ternary Copper Tungsten Sulfide (Cu2WS4) Nanoparticles Obtained through a Solvothermal Approach: A Bi-Functional Electrocatalyst for the Hydrogen Evolution Reaction (HER) and Oxygen Evolution Reaction (OER)

In this work, Cu₂WS₄ nanoparticles have been synthesized via a solvothermal decomposition approach using a heterobimetallic single source precursor, WCu₂S₄(PPh₃)₃. The single source precursor, WCu₂S₄(PPh₃)₃, has been characterized using multinuclear NMR spectroscopy, while Cu₂WS₄ nanoparticles have been characterized by powder X-ray diffraction (PXRD) for which Rietveld refinement has been performed to authenticate the lattice structure of the decomposed product, Cu₂WS₄. Furthermore, FESEM and EDAX analyses have been performed to assess the morphology and composition of Cu₂WS₄. An electrochemical study in acidic as well as basic media suggested that Cu₂WS₄ nanoparticles possess efficient bifunctional activity towards electrochemical hydrogen as well as oxygen evolution reactions. Linear sweep voltammetry (LSV) performed in 0.5 N H₂SO₄ indicates an onset potential for the HER of 462 mV and a Tafel slope of 140 mV dec⁻¹. While LSV performed in 0.1 M KOH indicates an onset potential for the OER of 190 mV and a Tafel Slope of 117 mV dec⁻¹.     

Fatigue Reliability Characterisation of Effective Strain Damage Model Using Extreme Value Distribution for Road Load Conditions

The aim of this paper is to characterise the fatigue reliability for various random strain loads under extreme value distribution while considering the cycle sequence effect condition in fatigue life prediction. The established strain–life models, i.e., Morrow and Smith–Watson–Topper, considered a mean stress effect and strain amplitude; nevertheless, it excluded the load sequence effect, which involves the fatigue crack closure that is subjected to overload or underload. A FESEM-EDX analysis is conducted to characterise the failure features that occurred on the leaf spring. A finite element is simulated to determine the critical region in order to obtain the strain load behaviour. In addition, the strain signal is captured experimentally at 500 Hz for 100 s under operating conditions for three different road loads based on the critical location obtained from the finite element analysis. The fatigue life correlation shows that the Pearson correlation coefficients are greater than 0.9, which indicates the effective strain damage model is linearly correlated with the strain–life models. The fatigue life data are modelled using extreme value distribution by considering the random strain loads as extreme data. The reliability rate for the fatigue life is reported to be more than 0.59 within the hazard rate range of 9.6 × 10⁻⁸ to 1.2 × 10⁻⁷ based on the mean cycle to the failure point. Hence, the effective strain damage model is proposed for a fatigue reliability assessment under extreme conditions with higher reliability and provides fatigue life prediction when subjected to cycle sequence effects.     

Aramid-wrapped CNT hybrid sol–gel sorbent for polycyclic aromatic hydrocarbons

This work describes the preparation of an analytical microextraction sorbent using a simple and versatile sol–gel hybrid composite, i.e., aramid oligomers wrapping multi-walled carbon nanotubes (CNTs) covalently bonded to a porous silica network. To overcome the inherent shortcomings of the CNTs'' solubility and dispersion in both organic phases and in the sol–gel solution, the outer surface of the CNTs was initially functionalized with carboxylic acid groups and then reacted with both aramid oligomers and 3-aminopropyl triethoxysilane (APTES). The obtained sorbent was characterized by FT-IR, scanning electron microscopy (SEM), and thermogravimetric analysis (TGA). Using sol–gel chemistry, the functionalized CNTs were coated onto SPME fibers and used in conjunction with GC-MS for the analysis of polycyclic aromatic hydrocarbons (PAHs) in water and soil samples. Excellent repeatability (run-to-run RSD% ∼ 8) and reproducibility (fiber-to-fiber RSD% ∼ 6) were achieved in addition to low LODs (0.10–0.30 ng mL⁻¹) and noticeable recovery%. The present method of sorbent preparation led to enhanced thermal and chemical stabilities, a long sorbent lifetime and good affinity towards PAHs. Moreover, the present sorbent enhanced the extraction capability by more than 30% compared to that of commercially available PDMS counterparts.

This work describes the preparation of an analytical microextraction sorbent using a simple and versatile sol–gel hybrid composite, i.e., aramid oligomers wrapping multi-walled carbon nanotubes (CNTs) covalently bonded to a porous silica network.     

Bile acid indices as biomarkers for liver diseases I: Diagnostic markers

BACKGROUNDHepatobiliary diseases result in the accumulation of toxic bile acids (BA) in the liver, blood, and other tissues which may contribute to an unfavorable prognosis.AIMTo discover and validate diagnostic biomarkers of cholestatic liver diseases based on the urinary BA profile.METHODSWe analyzed urine samples by liquid chromatography-tandem mass spectrometry and compared the urinary BA profile between 300 patients with hepatobiliary diseases vs 103 healthy controls by statistical analysis. The BA profile was characterized using BA indices, which quantifies the composition, metabolism, hydrophilicity, and toxicity of the BA profile. BA indices have much lower inter- and intra-individual variability compared to absolute concentrations of BA. In addition, BA indices demonstrate high area under the receiver operating characteristic curves, and changes of BA indices are associated with the risk of having a liver disease, which demonstrates their use as diagnostic biomarkers for cholestatic liver diseases.RESULTSTotal and individual BA concentrations were higher in all patients. The percentage of secondary BA (lithocholic acid and deoxycholic acid) was significantly lower, while the percentage of primary BA (chenodeoxycholic acid, cholic acid, and hyocholic acid) was markedly higher in patients compared to controls. In addition, the percentage of taurine-amidation was higher in patients than controls. The increase in the non-12α-OH BA was more profound than 12α-OH BA (cholic acid and deoxycholic acid) causing a decrease in the 12α-OH/ non-12α-OH ratio in patients. This trend was stronger in patients with more advanced liver diseases as reflected by the model for end-stage liver disease score and the presence of hepatic decompensation. The percentage of sulfation was also higher in patients with more severe forms of liver diseases.CONCLUSIONBA indices have much lower inter- and intra-individual variability compared to absolute BA concentrations and changes of BA indices are associated with the risk of developing liver diseases.