From Jane Doe to Sofia: DNA Extraction Protocol from Bones and Teeth without Liquid Nitrogen for Identifying Skeletal Remains.

DNA analysis plays a crucial role in forensic investigations, helping in criminal cases, missing persons inquiries, and archaeological research. This study focuses on the DNA concentration in different skeletal elements to improve human identification efforts. Ten cases of unidentified skeletal remains brought to the Institute of Forensic Medicine in Timisoara, Romania, underwent DNA analysis between 2019 and 2023. The results showed that teeth are the best source for DNA extraction as they contain the highest concentration of genetic material, at 3.68 ng/µL, compared to the petrous temporal bone (0.936 ng/µL) and femur bone (0.633 ng/µL). These findings highlight the significance of teeth in forensic contexts due to their abundant genetic material. Combining anthropological examination with DNA analysis enhances the understanding and precision of identifying human skeletal remains, thus advancing forensic science. Selecting specific skeletal elements, such as the cochlea or teeth, emerges as crucial for reliable genetic analyses, emphasizing the importance of careful consideration in forensic identification procedures. Our study concludes that automated DNA extraction protocols without liquid nitrogen represent a significant advancement in DNA extraction technology, providing a faster, more efficient, and less labor-intensive method for extracting high-quality DNA from damaged bone and tooth samples.

Performance of Zr-Based Metal-Organic Framework Materials as In Vitro Systems for the Oral Delivery of Captopril and Ibuprofen.

Zr-based metal-organic framework materials (Zr-MOFs) with increased specific surface area and pore volume were obtained using chemical (two materials, Zr-MOF1 and Zr-MOF3) and solvothermal (Zr-MOF2) synthesis methods and investigated via FT-IR spectroscopy, TGA, SANS, PXRD, and SEM methods. The difference between Zr-MOF1 and Zr-MOF3 lies in the addition of reactants during synthesis. Nitrogen porosimetry data indicated the presence of pores with average dimensions of ~4 nm; using SANS, the average size of the Zr-MOF nanocrystals was suggested to be approximately 30 nm. The patterns obtained through PXRD were characterized by similar features that point to well-crystallized phases specific for the UIO-66 type materials; SEM also revealed that the materials were composed of small and agglomerate crystals. Thermogravimetric analysis revealed that both materials had approximately two linker deficiencies per Zr6 formula unit. Captopril and ibuprofen loading and release experiments in different buffered solutions were performed using the obtained Zr-based metal-organic frameworks as drug carriers envisaged for controlled drug release. The carriers demonstrated enhanced drug-loading capacity and showed relatively good results in drug delivery. The cumulative percentage of drug release in phosphate-buffered solution at pH 7.4 was higher than that in buffered solution at pH 1.2. The release rate could be controlled by changing the pH of the releasing solution. Different captopril release behaviors were observed when the experiments were performed using a permeable dialysis membrane.

Design of a Synthetic Long Peptide Vaccine Targeting HPV-16 and -18 Using Immunoinformatic Methods.

Human papillomavirus types 16 and 18 cause the majority of cervical cancers worldwide. Despite the availability of three prophylactic vaccines based on virus-like particles (VLP) of the major capsid protein (L1), these vaccines are unable to clear an existing infection. Such infected persons experience an increased risk of neoplastic transformation. To overcome this problem, this study proposes an alternative synthetic long peptide (SLP)-based vaccine for persons already infected, including those with precancerous lesions. This new vaccine was designed to stimulate both CD8+ and CD4+ T cells, providing a robust and long-lasting immune response. The SLP construct includes both HLA class I- and class II-restricted epitopes, identified from IEDB or predicted using NetMHCPan and NetMHCIIPan. None of the SLPs were allergenic nor toxic, based on in silico studies. Population coverage studies provided 98.18% coverage for class I epitopes and 99.81% coverage for class II peptides in the IEDB world population's allele set. Three-dimensional structure ab initio prediction using Rosetta provided good quality models, which were assessed using PROCHECK and QMEAN4. Molecular docking with toll-like receptor 2 identified potential intrinsic TLR2 agonist activity, while molecular dynamics studies of SLPs in water suggested good stability, with favorable thermodynamic properties.

QSAR by Minimal Topological Difference[s]: Post-Modern Perspectives.

In the context of reconsidering the Quantitative Structure-Activity Relationship (QSAR) methods at the economical level, namely the optimization rules of OECD, the present review unfolds the key features of Minimal Sterical, Monte-Carlo and Minimal Topological Difference (MTD) methods, developed for quantitative treatment of the relations between biological activity of organic chemical compounds (drugs, pesticides, and so on) and their structures. The initial Minimal Steric Difference (MSD) is completed by the three-dimensional variant of the MTD method, being the last one referred to here, while the main principles of validating and guiding a viable QSAR method verified by the analytical-automated MTD, thus enlarging the perspectives of understanding the chemical-biological interaction at the level of ligand-receptor sites, cavity, and walls, with a true service to the future adaptive molecular design.

Prevention of Deficit in Neuropsychiatric Disorders through Monitoring of Arsenic and Its Derivatives as Well as Through Bioinformatics and Cheminformatics.

Neuropsychiatric disorders are induced by various risk factors, including direct exposure to environmental chemicals. Arsenic exposure induces neurodegeneration and severe psychiatric disorders, but the molecular mechanisms by which brain damage is induced are not yet elucidated. Our aim is to better understand the molecular mechanisms of arsenic toxicity in the brain and to elucidate possible ways to prevent arsenic neurotoxicity, by reviewing significant experimental, bioinformatics, and cheminformatics studies. Brain damage induced by arsenic exposure is discussed taking in account: the correlation between neuropsychiatric disorders and the presence of arsenic and its derivatives in the brain; possible molecular mechanisms by which arsenic induces disturbances of cognitive and behavioral human functions; and arsenic influence during psychiatric treatments. Additionally, we present bioinformatics and cheminformatics tools used for studying brain toxicity of arsenic and its derivatives, new nanoparticles used as arsenic delivery systems into the human body, and experimental ways to prevent arsenic contamination by its removal from water. The main aim of the present paper is to correlate bioinformatics, cheminformatics, and experimental information on the molecular mechanism of cerebral damage induced by exposure to arsenic, and to elucidate more efficient methods used to reduce its toxicity in real groundwater.

Chemical Structure-Biological Activity Models for Pharmacophores' 3D-Interactions.

Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding) and quantitative (for predicting) mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR) offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD) as the revived precursor for comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine congeners' (HEPT ligands) antiviral activity against Human Immunodeficiency Virus of first type (HIV-1) and new pharmacophores in treating severe genetic disorders (like depression and psychosis), respectively, all involving 3D pharmacophore interactions.

Calcium Channel Blockers--Benefits Upon Vascular Biology in Hypertensive Patients.

Calcium channel blockers (CCB) are widely used in cardiovascular medicine expressing high hopes upon decreasing cardiovascular risk, morbidity and mortality. Here, the potency of CCBs on 58 Romanian asymptomatic hypertensive patients, with no atherothrombotic cardiovascular disease, was studied by clinical and in silico methods. In our study, arterial elasticity/stiffness was assessed; anthropometric, metabolic (lipidic) parameters were quantified. We concluded that lercanidipine 10 mg once daily, during three weeks, is able to dramatically improve central aortic systolic blood pressure, aortic pulse wave velocity. Lipid profile improvement is an essential condition to improve elastic vascular properties in order to decrease the risk for further cardiovascular events. Besides, the potency of lercanidipine is expressed as the contribution of molecular descriptors (van der Waals and solvent accessible surface areas), electronic (molecular polarisability) and hydrophobic (water/octanol partition coefficient) by means of blocker effect on calcium channel, compared with cilnidipine and other 30 dihydropyridines, using molecular simulation techniques.

More effective DPP4 inhibitors as antidiabetics based on sitagliptin applied QSAR and clinical methods.

Xanthine-based molecules such as serine protease dipeptidyl peptidase 4 (DPP4) inhibitors are compounds often used in improving glycemic control in type 2 diabetic patients and also used for their effects as mild stimulants and as bronchodilators, notably in treating asthma symptoms. Here, we aim to better understand the molecular features affecting activity of xanthine-based DPP4 inhibitors such as sitagliptin and related compounds and use these features to de novo predict improved sitagliptin derivatives. To this end, we performed a clinical study to examine the efficacy and safety of once-daily 100 mg oral sitagliptin as monotherapy in Romanian patients with type 2 diabetes. This study indicates that sitagliptin effectively decreases the glycemic level and provides very good glycemic equilibrium. To predict putative new drugs with identical pharmacological effects at lower dosages, we generate QSAR models based on compound series containing 35 DPP4 inhibitors. We establish that the physicochemical parameters critical for DPP4 inhibitory activity are: hydrophobicity described by the logarithm of the octanol/water partition coefficient, counts of rotatable bonds, hydrogen bond donor and acceptor atoms, and topological polar surface area. The predictive power of our QSAR models is indicated by significant values of statistical coefficients: cross-validated correlation q2 (0.77), fitted correlation coefficient r2 (0.85) and standard error of prediction (0.34). Based on the established QSAR equations, we propose and analyse 19 new sitagliptin derivatives with possibly improved pharmacological effect as DPP4 inhibitors.

Introducing catastrophe-QSAR. Application on modeling molecular mechanisms of pyridinone derivative-type HIV non-nucleoside reverse transcriptase inhibitors.

The classical method of quantitative structure-activity relationships (QSAR) is enriched using non-linear models, as Thom's polynomials allow either uni- or bi-variate structural parameters. In this context, catastrophe QSAR algorithms are applied to the anti-HIV-1 activity of pyridinone derivatives. This requires calculation of the so-called relative statistical power and of its minimum principle in various QSAR models. A new index, known as a statistical relative power, is constructed as an Euclidian measure for the combined ratio of the Pearson correlation to algebraic correlation, with normalized t-Student and the Fisher tests. First and second order inter-model paths are considered for mono-variate catastrophes, whereas for bi-variate catastrophes the direct minimum path is provided, allowing the QSAR models to be tested for predictive purposes. At this stage, the max-to-min hierarchies of the tested models allow the interaction mechanism to be identified using structural parameter succession and the typical catastrophes involved. Minimized differences between these catastrophe models in the common structurally influential domains that span both the trial and tested compounds identify the "optimal molecular structural domains" and the molecules with the best output with respect to the modeled activity, which in this case is human immunodeficiency virus type 1 HIV-1 inhibition. The best molecules are characterized by hydrophobic interactions with the HIV-1 p66 subunit protein, and they concur with those identified in other 3D-QSAR analyses. Moreover, the importance of aromatic ring stacking interactions for increasing the binding affinity of the inhibitor-reverse transcriptase ligand-substrate complex is highlighted.

3D-QSAR design of new escitalopram derivatives for the treatment of major depressive disorders.

Antidepressants are psychiatric agents used for the treatment of different types of depression being at present amongst the most commonly prescribed drug, while their effectiveness and adverse effects are the subject of many studies and competing claims. Having studied five QSAR models predicting the biological activities of 18 antidepressants, already approved for clinical treatment, in interaction with the serotonin transporter (SERT), we attempted to establish the membrane ionsâ contributions (sodium, potassium, chlorine and calcium) supplied by donor/acceptor hydrogen bond character and electrostatic field to the antidepressant activity. Significant cross-validated correlation q(2) (0.5â0.6) and the fitted correlation r(2) (0.7â0.82) coefficients were obtained indicating that the models can predict the antidepressant activity of compounds. Moreover, considering the contribution of membrane ions (sodium, potassium and calcium) and hydrogen bond donor character, we have proposed a library of 24 new escitalopram structures, some of them probably with significantly improved antidepressant activity in comparison with the parent compound.