Antibody drug conjugates of cleavable amino-alkyl and aryl maytansinoids

Natural products have been used for many medicinal purposes for centuries. Antibody drug conjugates (ADCs) have utilized this rich source of small molecule therapeutics to produce several clinically useful treatments. ADCs based on the natural product maytansine have been successful clinically. The authors further the utility of the anti-cancer natural product maytansine by developing efficacious payloads and linker-payloads for conjugating to antibodies. The success of our approach was realized in the EGFRvIII targeting ADC EGFRvIII-16. The ADC was able to regress tumors in 2 tumor models (U251/EGFRvIII and MMT/EGFRvIII). When compared to a positive control ADC, the efficacy observed was similar or improved while the isotype control ADCs had no effect.     

Oxygen pressures in the interstitial space and their relationship to those in the blood plasma in resting skeletal muscle.

This study compared oxygen pressures (Po(2)), measured by oxygen-dependent quenching of phosphorescence, in the intravascular (blood plasma) space in the muscle with those in the interstitial (pericellular) space. Our hypothesis was that the capillary wall would not significantly impede oxygen diffusion from the blood plasma to the pericellular space. A new near-infrared oxygen sensitive probe, Oxyphor G3, was used to obtain oxygen distributions in the interstitial space. Oxyphor G3 is a Pd-tetrabenzoporphyrin encapsulated inside generation 2 poly-arylglycine (AG) dendrimer. The periphery of the dendrimer is modified with oligoethylene glycol residues (average molecular weight 350) to make the probe water soluble and biologically inert. Oxyphor G3 was injected into thigh muscle using a 30-gauge needle. Histograms of the Po(2) in the interstitial space were measured in awake and anesthetized animals and compared with those for Oxyphor G2 in the intravascular (blood plasma) space. For awake mice, the lowest 10% of Po(2) values for the interstitial and intravascular spaces (believed to represent capillary bed) were not significantly different [23.8 (SD 4.5) and 25 Torr (SD 4.3), respectively], whereas, in isoflurane-anesthetized mice, there was a small but significant (P = 0.01) difference [20.4 (SD 6.3) and 27.9 Torr (SD 3.5), respectively]. The peak values for the histograms for the interstitial space in awake and isoflurane-anesthetized mice were 40.8 (SD 7.5) and 36.9 Torr (SD 8.3), respectively, whereas those for the intravascular space were 52.2 (SD 4.9) and 55.9 Torr (SD 8.4), respectively, showing no significant difference due to isoflurane anesthesia. The histograms for the intravascular space were significantly wider, with more contribution at higher Po(2) values. A different anesthetic, ketamine plus xylazine injected intraperitoneally, caused a marked decrease in the tissue Po(2) values in both spaces, with the time course and extent of the decrease dependent on the time after injection and variable among mice. It was, therefore, not further used.     

Machine learning-based risk factor analysis and prevalence prediction of intestinal parasitic infections using epidemiological survey data

BackgroundPrevious epidemiological studies have examined the prevalence and risk factors for a variety of parasitic illnesses, including protozoan and soil-transmitted helminth (STH, e.g., hookworms and roundworms) infections. Despite advancements in machine learning for data analysis, the majority of these studies use traditional logistic regression to identify significant risk factors.MethodsIn this study, we used data from a survey of 54 risk factors for intestinal parasitosis in 954 Ethiopian school children. We investigated whether machine learning approaches can supplement traditional logistic regression in identifying intestinal parasite infection risk factors. We used feature selection methods such as InfoGain (IG), ReliefF (ReF), Joint Mutual Information (JMI), and Minimum Redundancy Maximum Relevance (MRMR). Additionally, we predicted children’s parasitic infection status using classifiers such as Logistic Regression (LR), Support Vector Machines (SVM), Random Forests (RF) and XGBoost (XGB), and compared their accuracy and area under the receiver operating characteristic curve (AUROC) scores. For optimal model training, we performed tenfold cross-validation and tuned the classifier hyperparameters. We balanced our dataset using the Synthetic Minority Oversampling (SMOTE) method. Additionally, we used association rule learning to establish a link between risk factors and parasitic infections.Key findingsOur study demonstrated that machine learning could be used in conjunction with logistic regression. Using machine learning, we developed models that accurately predicted four parasitic infections: any parasitic infection at 79.9% accuracy, helminth infection at 84.9%, any STH infection at 95.9%, and protozoan infection at 94.2%. The Random Forests (RF) and Support Vector Machines (SVM) classifiers achieved the highest accuracy when top 20 risk factors were considered using Joint Mutual Information (JMI) or all features were used. The best predictors of infection were socioeconomic, demographic, and hematological characteristics.ConclusionsWe demonstrated that feature selection and association rule learning are useful strategies for detecting risk factors for parasite infection. Additionally, we showed that advanced classifiers might be utilized to predict children’s parasitic infection status. When combined with standard logistic regression models, machine learning techniques can identify novel risk factors and predict infection risk.     

An EEG analysis of the structural interrelationships of the thalamofrontal system in the recovery of conditioned-reflex behavior in amygdalectomized rats]

Unilateral damage of the baso-lateral amygdala in white non-strain rats led to decrease of the number of instrumental responses and increase of their latencies in the course of the first two weeks after the surgery. This period was characterized by increase of the delta- and theta-frequency bands in electrograms of the orbito-frontal cortex and dorsomedial nucleus of the thalamus. During the next two weeks this increase was observed only in the orbito-frontal cortex. Functional reorganization of interconnections between the limbic and thalamo-cortical brain structures is supposed to underly phenomena observed.     

Electrophysiologic and behavioral changes after destruction of the basolateral nucleus of the amygdala in the rat

In experiments on rats the influence was studied of unilateral destruction of the basolateral amygdala on heterogeneous conditioned reflexes and focal potentials (FPs) of the dorso-medial thalamic nucleus, elicited by stimulation of the peri-amygdalar cortex. It is shown that reduction during 3-4 postsurgery weeks of the number of goal-directed reactions and increase of their latencies are accompanied by suppression of the evoked activity, testifying to disturbance of functional interaction between the structures of limbic and thalamic levels. Changes of FPs parameters, reflecting the establishment of new functional interrelations between the centres of the lesioned system excelled in duration compensatory restoration of properties of conditioned reflexes.