Improved cisplatin delivery in cervical cancer cells by utilizing folate-grafted non-aggregated gelatin nanoparticles

PurposeCisplatin is highly effective in the treatment of cervical cancer. However, in therapeutic doses, cisplatin induces several adverse effects due to undesirable tissue distribution. Therefore, it is worth targeting cisplatin in cervical cancer cells by implicating non-aggregated ligand-modified nanotherapeutics.Methods and resultsHere, we report the preparation of non-aggregated folic acid-conjugated gelatin nanoparticles of cisplatin (Cis-GNs-FA) by two-step desolvation method with mean particle size of 210.6 ± 9.6 nm and 140.5 ± 10.9 nm for Cis-GNs to improve the drug delivery in cervical cancer, HeLa cells. FTIR and DSC spectra confirmed the presence and stability of cisplatin in gelatin matrix. Furthermore, amorphization of cisplatin in nanoparticles was ascertained by PXRD. Drug release followed a first-order release kinetic at both pH ∼ 5.6 (cervical cancer pH) and pH ∼ 7.4. In addition, a significant (P < 0.05) decrease in IC₅₀ value (8.3 μM) and enhanced apoptosis were observed in HeLa cells treated with Cis-GNs-FA as compared to Cis-GNs (15.1 μM) and cisplatin solution (40.2 μM). In contrast, A549 lung cancer cells did not discriminate between Cis-GNs-FA and Cis-GNs due to the absence of folate receptors-α (FR-α). Consistently, higher cellular uptake, 80.54 ± 7.60% was promoted by Cis-GNs-FA significantly (two-way ANOVA, P < 0.05) greater than 51.68 ± 9.78%, by Cis-GNs. This was also illustrated by CLSM images, which indicated that Cis-GNs-FA preferably accumulated in the cytoplasm of HeLa cells nearby nucleus by following receptor-mediated endocytosis pathway as compared to Cis-GNs.ConclusionTherefore, Cis-GNs-FA warrants further in-depth in vitro and in vivo investigations to scale up the technology for clinical translation.     

Automatic Identification of Web-Based Risk Markers for Health Events

Background

The escalating cost of global health care is driving the development of new technologies to identify early indicators of an individual’s risk of disease. Traditionally, epidemiologists have identified such risk factors using medical databases and lengthy clinical studies but these are often limited in size and cost and can fail to take full account of diseases where there are social stigmas or to identify transient acute risk factors.

Objective

Here we report that Web search engine queries coupled with information on Wikipedia access patterns can be used to infer health events associated with an individual user and automatically generate Web-based risk markers for some of the common medical conditions worldwide, from cardiovascular disease to sexually transmitted infections and mental health conditions, as well as pregnancy.

Methods

Using anonymized datasets, we present methods to first distinguish individuals likely to have experienced specific health events, and classify them into distinct categories. We then use the self-controlled case series method to find the incidence of health events in risk periods directly following a user’s search for a query category, and compare to the incidence during other periods for the same individuals.

Results

Searches for pet stores were risk markers for allergy. We also identified some possible new risk markers; for example: searching for fast food and theme restaurants was associated with a transient increase in risk of myocardial infarction, suggesting this exposure goes beyond a long-term risk factor but may also act as an acute trigger of myocardial infarction. Dating and adult content websites were risk markers for sexually transmitted infections, such as human immunodeficiency virus (HIV).

Conclusions

Web-based methods provide a powerful, low-cost approach to automatically identify risk factors, and support more timely and personalized public health efforts to bring human and economic benefits.     

From the Cover: Synchronization in human musical rhythms and mutually interacting complex systems

Though the music produced by an ensemble is influenced by multiple factors, including musical genre, musician skill, and individual interpretation, rhythmic synchronization is at the foundation of musical interaction. Here, we study the statistical nature of the mutual interaction between two humans synchronizing rhythms. We find that the interbeat intervals of both laypeople and professional musicians exhibit scale-free (power law) cross-correlations. Surprisingly, the next beat to be played by one person is dependent on the entire history of the other person’s interbeat intervals on timescales up to several minutes. To understand this finding, we propose a general stochastic model for mutually interacting complex systems, which suggests a physiologically motivated explanation for the occurrence of scale-free cross-correlations. We show that the observed long-term memory phenomenon in rhythmic synchronization can be imitated by fractal coupling of separately recorded or synthesized audio tracks and thus applied in electronic music. Though this study provides an understanding of fundamental characteristics of timing and synchronization at the interbrain level, the mutually interacting complex systems model may also be applied to study the dynamics of other complex systems where scale-free cross-correlations have been observed, including econophysics, physiological time series, and collective behavior of animal flocks.In his book Musicophilia, neurologist Oliver Sacks writes: “In all societies, a primary function of music is collective and communal, to bring and bind people together. People sing together and dance together in every culture, and one can imagine them having done so around the first fires, a hundred thousand years ago” (1). Sacks adds, “In such a situation, there seems to be an actual binding of nervous systems accomplished by rhythm” (2). These thoughts lead to the question: Is there any underlying and quantifiable structure to the subjective experience of “musical binding”? Here, we examine the statistical nature of musical binding (also referred to as musical coupling) when two humans play rhythms in synchrony.Every beat a single (noninteracting) layperson or musician plays is accompanied by small temporal deviations from the exact beat pattern, i.e., even a trained musician will hit a drum beat slightly ahead or behind the metronome (with a SD of typically 5–15 ms). Interestingly, these deviations are statistically dependent and exhibit long-range correlations (LRC) (3, 4). Listeners significantly prefer music mirroring long-range correlated temporal deviations over uncorrelated (white noise) fluctuations (5, 6). LRC are also inherent in the reproduction of both spatial and temporal intervals of single subjects (4, 7–9) and in musical compositions, such as pitch fluctuations (a simple example of pitch fluctuations is a melody) (10, 11) and note lengths (12). The observation of power law correlations in fluctuations of pitch and note length in compositions reflects a hierarchical, self-similar structure in these compositions.In this article, we examine rhythmic synchronization, which is at the foundation of musical interaction, from orchestral play to audience hand-clapping (13). More specifically, we show that the interbeat intervals (IBIs) of two subjects synchronizing musical rhythms exhibit long-range cross-correlations (LRCCs), which appears to be a general phenomenon given that these LRCC were found both in professional musicians and in laypeople.The observation of LRCCs may point to characteristics of criticality in the dynamics of the considered complex system. LRCCs are characterized by a power law decay of the cross-correlation function and indicate that the two time series of IBIs form a self-similar (fractal) structure. Here, self-similarity implies that trends in the IBIs are likely to repeat on different timescales, i.e., patterns of IBI fluctuations of one musician tend to reproduce in a statistically similar way at a later time—even in the other musician’s play. A variety of complex systems exhibit LRCCs; examples include price fluctuations of the New York Stock Exchange (where the LRCCs become more pronounced during economic crises) (14–16), heartbeat and EEG fluctuations (15, 17), particles in a Lorentz channel (18), the binding affinity of proteins to DNA (15), schools of fish (19), and the collective response of starling flocks (20, 21). The origin of collective dynamics and LRCCs based on local interactions often appears elusive (20), and is the focus of current research (19, 21). Of particular interest are the rules of interactions of the individuals in a crowd (22, 23) and transitions to synchronized behavior (16, 24). We introduce a stochastic model for mutually interacting complex systems (MICS) that generates LRCCs and provides a physiologically motivated explanation for the surprising presence of long-term memory in the cross-correlations of musical performances.Interbrain synchronization has received growing attention recently, including studies of interpersonal synchronization (see ref. 4 for an overview), coordination of speech rhythm (25), social interactions (26), cortical phase synchronization while playing guitar in duets (27, 28), and improvisation in classical music performances (29).Notably, the differences between the beats of two musicians are on the order of only a few milliseconds, not much larger than the typical duration of a single action potential (∼1 ms). The neurophysical mechanisms of timing in the millisecond range are still widely open (30, 31). EEG oscillatory patterns are associated with error prediction during music performance (32). Fine motor skills, such as finger-tapping rhythm and rate, are used to establish an early diagnosis of Huntington disease (33). The neurological capacity to synchronize with a beat may offer therapeutic applications for Parkinson disease, but the mechanisms are unknown to date (34). This study offers a statistical framework that may help to understand these mechanisms.     

The cognitive profile of occipital lobe epilepsy and the selective association of left temporal lobe hypometabolism with verbal memory impairment

We investigated the cognitive profile of structural occipital lobe epilepsy (OLE) and whether verbal memory impairment is selectively associated with left temporal lobe hypometabolism on [18F]‐fluorodeoxyglucose positron emission tomography (FDG‐PET). Nine patients with OLE, ages 8–29 years, completed presurgical neuropsychological assessment. Composite measures were calculated for intelligence quotient (IQ), speed, attention, verbal memory, nonverbal memory, and executive functioning. In addition, the Wisconsin Card Sorting Test (WCST) was used as a specific measure of frontal lobe functioning. Presurgical FDG‐PET was analyzed with statistical parametric mapping in 8 patients relative to 16 healthy volunteers. Mild impairments were evident for IQ, speed, attention, and executive functioning. Four patients demonstrated moderate or severe verbal memory impairment. Temporal lobe hypometabolism was found in seven of eight patients. Poorer verbal memory was associated with left temporal lobe hypometabolism (p = 0.002), which was stronger (p = 0.03 and p = 0.005, respectively) than the association of left temporal lobe hypometabolism with executive functioning or with performance on the WCST. OLE is associated with widespread cognitive comorbidity, suggesting cortical dysfunction beyond the occipital lobe. Verbal memory impairment is selectively associated with left temporal lobe hypometabolism in OLE, supporting a link between neuropsychological dysfunction and remote hypometabolism in focal epilepsy. A PowerPoint slide summarizing this article is available for download in the Supporting Information section here .     

Overspill avalanching in a dense reservoir network

Sustainability of communities, agriculture, and industry is strongly dependent on an effective storage and supply of water resources. In some regions the economic growth has led to a level of water demand that can only be accomplished through efficient reservoir networks. Such infrastructures are not always planned at larger scale but rather made by farmers according to their local needs of irrigation during droughts. Based on extensive data from the upper Jaguaribe basin, one of the world's largest system of reservoirs, located in the Brazilian semiarid northeast, we reveal that surprisingly it self-organizes into a scale-free network exhibiting also a power-law in the distribution of the lakes and avalanches of discharges. With a new self-organized-criticality-type model we manage to explain the novel critical exponents. Implementing a flow model we are able to reproduce the measured overspill evolution providing a tool for catastrophe mitigation and future planning.