Teenage Sleep vs. Its Biggest Impact – Cellphones

    It’s 1 am, and you were supposed to be asleep for your exam at 8 am the next morning. The artificial sun is pouring into your eyes in your dark room. After noticing the time, you decide to go to sleep, but it takes you an hour. The next morning, you walk into your class groggy, and you end up passing the exam with a C. Not the grade you wanted or studied for.

    I recently attended a seminar by Dr. Stephanie Crowley, who talked about her article about adolescent sleep called “An update on adolescent sleep: New evidence informing the perfect storm model”. In this seminar, she mentioned that there are two internal systems that control sleep. In adolescence, sleep is naturally pushed later, but things such as societal pressure require early wake-up times. The homeostatic system that builds pressure to sleep builds more slowly than in childhood. This results in the internal clock, circadian rhythm, shifting later, causing drowsiness to start later. Falling asleep later causes waking up later to get the required nine hours of sleep. This is a problem since school requires students to be at school before 8 am, and if students don’t get tired earlier, they won’t be able to get enough sleep. Along with this, there is light from screens. This light causes your brain not to produce melatonin.

    In a 2023 literature review called “The Influence of Smartphones on Adolescent Sleep: A Systematic Literature Review,” the researchers reviewed how smartphones specifically influenced sleep quality in adolescents. They found that adolescents who have a smartphone sleep fewer hours compared to those without one. 97% of teenagers involved in a study used some screen before bed, with the most common device being a smartphone. The activities on these devices such as texting or scrolling on social media keeps the brain active. Along with that, the blue light emitted from a screen reduces melatonin secretion, causing a later sleep time. Multiple researchers have found a similar conclusion. Smartphones cause cognitive arousal during a time that the brain should be winding down. A bad night’s sleep has been linked to poor sleep, depressive mood, diminished coping abilities, and reduced academic performance.

    This links directly to Dr. Crowley’s talk about the “perfect storm.” Not getting the right amount of sleep has a ripple effect on the rest of life. There are multiple factors that combine to make adolescent sleep worse. Smartphone usage before bed keeps the brain up and doesn’t let it do what it has done for thousands of years. School requires teenagers to wake up early, while they fall asleep later due to biology. As a society, there would need to be shifts to better help and fit the adolescent sleep schedule. Adolescents can also help themselves by not using phones before bed and starting the natural cycle of sleep.

References

Crowley, Stephanie J., et al. “An Update on Adolescent Sleep: New Evidence Informing the Perfect Storm Model.” Journal of Adolescence, vol. 67, no. 67, Aug. 2018, pp. 55–65, https://doi.org/10.1016/j.adolescence.2018.06.001.

Sofia de Sá, et al. “The Influence of Smartphones on Adolescent Sleep: A Systematic Literature Review.” Nursing Reports, vol. 13, no. 2, Apr. 2023, pp. 612–21, https://doi.org/10.3390/nursrep13020054.

Brain-Computer Interfaces: Privacy, Consent, and Control

    In his recent talk, Joe Vukov explored brain-computer interfaces and their ethical implications, focusing mainly on issues of autonomy and privacy. He discussed a study by Hemmings Wu et al. on a closed-loop brain-computer interface (BCI) designed to treat loss of control eating in patients for whom previous treatment had been unsuccessful. The system detects neural signals associated with unhealthy eating behaviors originating in the nucleus accumbens and responds by delivering deep brain stimulation via the RNS System, preventing these impulses from developing into physical actions. As a professor of philosophy, Vukov’s main concern was with the autonomy and privacy retained by patients who opted in to treatments such as these. Because the system operates in a closed loop, its effects are not externally visible, making it difficult for patients to monitor, evaluate, or end the treatment once it begins. As Vukov discussed, this type of automated control risks limiting a patient’s ability to consent in real time or withdraw from treatment, drawing attention to the decrease in direct control that users will have as BCIs become more effective. He contrasts this with open-loop systems that translate the neural signals into external actions, such as speech or movement of a limb, introducing the concern that private thoughts could be continuously monitored or exposed without sufficient user control.

    In a new study, Erin M. Kunz et al. investigated the use of BCIs to decode inner speech in individuals with paralysis as a means to restore communication. For this study, four participants from the BrainGate2 trial were recruited, each with varying abilities to produce speech or communicate with others. In order to study the neural representations of their speech, microelectrode arrays were placed in the precentral gyrus of each participant. This area of the motor cortex produces neural activity associated with inner speech, perceived speech, and reading. After performing various tasks, some of the participants’ results showed a decoding accuracy for inner and perceived speech that was the same or better than attempted speech. It was also found that the neural representations of attempted and inner speech overlapped, showing a correlation in neural firing rates. This showed that words are encoded similarly across different behaviors, but also raises the possibility of unintentionally decoding the private inner speech of users. To address this concern, the researchers shifted focus to the difference in motor-intent signal between inner and attempted speech, which allowed decoders to distinguish between the behaviors and more accurately transcribe the desired speech. Another proposed solution to prevent unwanted decoding of inner speech is to employ a user-controlled keyword that could be said by users to “lock” and “unlock” the decoders, allowing inner speech to continue without being expressed out loud.

    The work done by Wu et al. and Kunz et al. highlight a promising direction for BCIs to continue making meaningful impacts on patients’ lives, while still preserving their privacy. Vukov’s discussion of BCIs show that systems that work independently of user acknowledgement risk diminishing user autonomy, regardless of therapeutic benefits. The research done by Kunz et al., however, demonstrates that it is possible for BCIs to be designed with user intention and control as a priority. By incorporating preventative measures such as motor-intent discernment or user-activated keywords, boundaries between private thoughts and deliberate speech can be established. These measures address the concerns raised by Vukov, suggesting that the ethical risks of BCIs are not necessarily a symptom of the technology, but rather, dependent on the design and implementation of each treatment system. Ensuring that users retain control over when and how their neural activity is interpreted will be essential for the further development and use of BCIs.

References:

Kunz, E. M., Abramovich Krasa, B., Kamdar, F., Avansino, D. T., Hahn, N., Yoon, S., Singh, A., Nason-Tomaszewski, S. R., Card, N. S., Jude, J. J., Jacques, B. G., Bechefsky, P. H., Iacobacci, C., Hochberg, L. R., Rubin, D. B., Williams, Z. M., Brandman, D. M., Stavisky, S. D., AuYong, N., … Willett, F. R. (2025). Inner speech in motor cortex and implications for speech neuroprostheses. Cell, 188(17). https://doi.org/10.1016/j.cell.2025.06.015 

Wu, H., Adler, S., Azagury, D. E., Bohon, C., Safer, D. L., Barbosa, D. A. N., Bhati, M. T., Williams, N. R., Dunn, L. B., Tass, P. A., Knutson, B. D., Yutsis, M., Fraser, A., Cunningham, T., Richardson, K., Skarpaas, T. L., Tcheng, T. K., Morrell, M. J., Roberts, L. W., Malenka, R. C., … Halpern, C. H. (2020). Brain-Responsive Neurostimulation for Loss of Control Eating: Early Feasibility Study. Neurosurgery, 87(6), 1277–1288. https://doi.org/10.1093/neuros/nyaa300

Can Artificial Intelligence Replace Humans in Histopathology?

    Convolutional neural networks (CNNs) have been an important component in the development of artificial intelligence (AI) and machine learning. It has been used in interpreting visual and spatial data and is being trained to perform in tasks related to image recognition, language processing, and tasks in the medical field (ScienceNewsToday). Delving into the medical field, pathology is a specialty that involves histology, which means studying tissue samples under a microscope to determine if the tissue is diseased. As artificial intelligence such as CNNs continue to evolve, it begs the question, can they replace humans in histopathology?

    During Dr. Baker’s talk at Loyola, he highlighted his own research regarding how deep convolutional models (DCNNs) struggle with the configuration of images compared to humans. In his paper, Dr. Baker highlighted how DCNNs focused more on color and texture than humans do (Baker). Additionally, in his presentation he went into detail into how the AI struggled with recognizing an image based on manipulation of the images’ border. For example, if the AI was given the silhouette of a cat, but if the border was turned into rigid edges rather than the normal “smooth” border, it would have more trouble recognizing what the image was in comparison to an image that was “Frankensteined” (chopped up and put together in a different configuration).

    It is necessary to make note of this distinction in the AI’s recognition of the image because cell tissues do not always maintain a constant shape or look, meaning that Dr. Baker findings could pose an issue of accuracy when using AI in pathology. A review article by Prasad et al. delves further into the use of AI in the field of histopathology. In their findings, CNNs were found to have consistent results in detecting and classifying cancerous tissue and had levels of accuracy similar to trained pathologists in controlled settings (Prasad). However, the article highlights an issue in which AI systems struggle with variability in staining, slide preparation, and tissue morphology (Prasad).

    This connects to Dr. Baker’s findings, as the AI relies heavily on texture and color rather than truly understanding structural features, which limits the effectiveness of CNNs when analyzing histological samples that are inconsistent or irregular (Baker). Furthermore, Prasad et al. highlight that while AI can assist with lowering workloads and increasing efficiency, as mentioned, it still lacks the adaptability needed to analyze irregularities that can be common in complex histopathological cases (Prasad). This shows that while AI can become an assistive tool in histopathology, its limitations in image interpretation demonstrate that humans in pathology are still necessary. However, rather than replacing pathologists, AI is more useful as a supportive tool that enhances accuracy and efficiency.

 

References

Editors of ScienceNewsToday. (2026, April 7). Convolutional neural networks: the science behind modern artificial intelligence. Science News Today. https://www.sciencenewstoday.org/convolutional-neural-networks-the-science-behind-modern-artificial-intelligence#google_vignette

Baker N, Elder JH. Deep learning models fail to capture the configural nature of human shape perception. iScience. 2022 Aug 11;25(9):104913. doi: 10.1016/j.isci.2022.104913. PMID: 36060067; PMCID: PMC9429800.

Prasad P, Khair AMB, Saeed M, Shetty N. Artificial Intelligence in Histopathology. J Pharm Bioallied Sci. 2024 Dec;16(Suppl 5):S4226-S4229. doi: 10.4103/jpbs.jpbs_727_24. Epub 2025 Jan 30. PMID: 40061791; PMCID: PMC11888715.

Peripheral Nerve Degeneration: How the Shock Affects Nerve Repair

     In science, there has been an uptick in the use of electrical stimulation to "biohack" our bodies and brains, whether it is to wake us up or for physical therapies. As a result, many new studies have emerged to understand the body's natural stimulation and create tech that mimics it to address a variety of issues. Currently, scientists are digging deeper to determine which frequencies are the best for humans to create devices specifically for nerve degeneration.   

         During our class, we heard from Dr. Vincent Chen, whose focus was Power Spectral Density (PSD). PSD describes the power of a time-domain signal, or how a random process can be distributed across different frequencies. Chen's hypothesis is as follows: we should manipulate the waveform shape to target NMDA and AMPA receptors, which act as switches that turn on nerve growth. His clinical success with "random noise" stimulation is due to the rich PSD. He also questions modern electrical stimulation methods, as square waves commonly used contain harmonics, echoes of higher frequencies hidden within the signal. With these higher frequencies, it makes it harder to determine which signal is most optimal for nerve regeneration actively. Just following the frequency to him is imprecise because the signal will degrade as soon as it passes through the skin and tissue. He notes that the nerve membrane acts as a capacitor, resisting sudden changes, so changing the type of wave could also affect the nerve differently.  By controlling the voltage gradient, he can target very specific receptors. Chen's approach is highly specific, and with the future of implantable devices, it blows all past nerve studies out of the water.   

A different study conducted by Dr. Lingmei Ni uses very traditional electrical stimulation approaches to alleviate the effects of nerve damage. Ni uses multiple types of electrical stimulation, including NMES (neuromuscular), and low-frequency pulses to contract the muscles, helping prevent muscle atrophy directly. TENS is transcutaneous, blocking the pain signals with varying high and low frequencies. FES is the functional frequency, which can help paralyzed limbs return to function. These therapies have been used for a long time, but Ni has clinical data on specific frequencies to support her statement. Her studies show that 1 hour of 20Hz stimulation is proficient to accelerate axon growth after carpal tunnel surgery. Electrical stimulation increases BDNF and cAMP, which act as fuel for a growing neuron. However, even Dr. Ni points out how there is no standard for electrical stimulation. It varies between patients, parts of the body, and at times can seem almost random.   

These studies, when read together, can be seen as the future of electrical stimulation. Dr. Ni has a wide breadth of knowledge and clinical data on electrical stimulation, helping regenerate axons faster. Dr. Chen takes it one step further; no longer will the frequency or even type of wave vary from person to person. Rather, using PSD data, we can build stimulators that do not cause nerve fatigue or unwanted pain from high-frequency harmonies. With this amalgamation of information, other scientists can take this data to build more biohacking devices to help humans live more comfortable lives following nerve degeneration.   

Chen, VincentC.-F., et al. "Accelerating Peripheral Nerve Regeneration Using Electrical  Stimulation of Selected Power Spectral Densities." Neural Regeneration Research,  vol. 17, no. 4, 2022, p. 781, https://doi.org/10.4103/1673-5374.322458.  

Ni, Lingmei, et al. "Electrical Stimulation Therapy for Peripheral Nerve Injury." National  Center for Biotechnology Information, U.S. National Library of Medicine, 23 Feb.  2023, pmc.ncbi.nlm.nih.gov/.  

Mirco-coil Stimulation

    Electrode stimulation, such as deep brain stimulation (DBS), has been utilized in treating pain, seizures, and improving movement. It is achieved by directly applying an electrical pulse to neural tissue via a surgically implanted electrode. There have been numerous successes in treatments involving electrode stimulation. However, there are also risks and limitations such as inaccurate neural targeting, potential for tissue damage which may trigger inflammation and immune responses, and corrosion, though rarely, of the electrode itself.

    A new magnetic stimulation technique that aims to improve and reduce the limitations of current electrode and magnetic stimulation techniques is micro-magnetic stimulation (µMS). µMS has been found to have improved specificity of neural stimulation, it utilizes biocompatible materials that reduce inflammation and allow for deep brain stimulation without damaging neural tissue.

    I recently had the pleasure of attending a talk by Dr. Ye Hui, a biomedical engineer focusing his research on electromagnetic stimulation. His talk focused on his recent paper: “Restore axonal conductance in a locally demyelinated axon with electromagnetic stimulation.” This paper investigated a demyelinated axon model built in NEURON under circular micro-coil stimulation. Demyelinated axons are often the result of neurodegenerative diseases, ischemic injuries, among other factors. Due to demyelination, neural signals are blocked or slowed depending on the severity of the demyelination. But how to restore those neural signals is not fully understood. The main findings of Dr.Ye’s paper found that subthreshold micro-coil stimulation, which is just below the needed stimulus to generate an action potential, combined with the depolarization of an action potential was enough to restore axonal conductance. In other words, a subthreshold stimulus saved the incoming action potential by giving it the boost to get past the demyelinated area and continue down the axon. Restoration was dependent on the amplitude and frequency of the stimulus which was found to have the most consistent results at 5000Hz. Dr.Ye’s research provides vital insights into not only the development of micro-coil stimulation, but also in treatments of focally segmented demyelination cases.

    An article titled: “Micro-Coil Neuromodulation at Single-Cell and Circuit Levels for Inhibiting Natural Neuroactivity, Neutralizing Electric Neural Excitation, and Suppressing Seizures.” by Kim et al., investigates µMS in both research and therapeutic applications. While Dr.Ye’s paper focused on restoring axonal conductance, this article focuses on precise neural inhibition, which is similarly not well understood, with µMS. Using cortical in vivo two-photon imaging, the researchers were able to find that µMS suppressed single cells and increasing µMS magnitude further increased the number of inhibited cells. µMS was also found to be able to suppress hyperactive neural firing caused by pharmacologically induced seizures, with more research, this could potentially lead to the suppression of epileptic seizures. These findings emphasize the importance of further researching the potential applications of µMS technology in clinical settings.

    As this is still a new field of research, there is still much that is not known about µMS.  However, both Dr.Ye, Kim et al., and many others are paving the way for µMS. One of the most compelling aspects of µMS research are its increasingly promising applications to not yet well understood branches of neuroscience. With its application to restoring axonal conductance in locally demyelinated axons to suppressing seizures, µMS has the potential to become a treatment for a variety of neurological disorders.

Citations: 

Ye, Hui et al. “Restore axonal conductance in a locally demyelinated axon with electromagnetic stimulation.” Journal of Neural Engineering vol. 22,1 016042. 14 Feb. 2025.

Ye, Hui et al. “Improving focality and consistency in micromagnetic stimulation.” Frontiers in computational neuroscience vol. 17, 1105505. 2 Feb. 2023.

Kim, Kayeon et al. “Micro-Coil Neuromodulation at Single-Cell and Circuit Levels for Inhibiting Natural Neuroactivity, Neutralizing Electric Neural Excitation, and Suppressing Seizures.” Advanced science (Weinheim, Baden-Wurttemberg, Germany) vol. 12,22 (2025): e2416771.